Cost-sensitivity Analyses for Gearbox Condition Monitoring Systems Offshore

Transcription

1 iii Master of Science Thesis Ton van den Broek Cost-sensitivity Analyses for Gearbox Condition Monitoring Systems Offshore

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3 Cost-sensitivity Analyses for Gearbox Condition Monitoring Systems Offshore Master of Science thesis For the degree of Master of Science in Sustainable Energy Technology at Eindhoven University of Technology Ton van den Broek January, 10, 2014 Wind Energy Research Group, Faculty of Aerospace Engineering - Delft University of Technology Department of Mechanical Engineering - Eindhoven University of Technology

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5 Eindhoven University of Technology Department of Mechanical Engineering The undersigned hereby certify that they have read and recommend to the Faculty of Applied Sciences (TNW) for acceptance a thesis entitled COST-SENSITIVITY ANALYSES FOR GEARBOX CONDITION MONITORING SYSTEMS OFFSHORE By Ton van den Broek In partial fulfilment of the requirements for the degree of Master of Science Sustainable Energy Technology Dated: 10 th of January, 2014 Supervisor: Readers: Prof. dr. G.J.W. van Bussel Dr. ir. J.J. Arts Dr. ir. M.B. Zaaijer

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7 iii Summary To compete more successfully with other sources of energy, a decrease in the costs of offshore wind energy needs to be achieved. Operation and maintenance costs represent a large share of these costs. In order to reduce these costs new developments and strategies are considered for operation and maintenance of wind turbine components. Condition monitoring systems (CMSs) could be a vital tool to decrease these costs, especially for expensive components such as a gearbox. This thesis focusses on the gearbox as it is the component, with one of the highest downtime per failure and failure costs. A literature study reveals that the replacement of a gearbox offshore might lead to months of downtime and costs might sum up to one million euro for a 6 MW wind turbine. In order to prevent such high costs, CMSs are assessed in this thesis. CMSs comprise of sensors providing data, which reflects the health status of the component. This data is subsequently analysed by a data-mining technique, capable of detecting trends and anomalies to predict upcoming failures. If the system is sufficiently accurate, a large failure can be prevented, which leads to significant savings in the lifetime gearbox maintenance costs A cost-benefit study is performed to determine the required performance of a CMS in order to be profitably implemented in an offshore wind turbine. The CMS performance is described by two parameters: one reflecting the ability of the system to prevent large gearbox failures, and a second parameter describing its ability to prevent waiting downtime, caused by weather window waiting time, spare part logistics and vessel mobilisation. Based on Monte Carlo simulations, the gearbox maintenance costs are quantified over the wind turbine lifetime. Subsequently a sensitivity analysis is performed. Results show that differences in gearbox failure rate and wind farm distance significantly affect the maintenance costs. Hence to break-even different performance requirements of the CMS are necessary. The model reveals that a system, capable of preventing large failures and/or preventing waiting downtime, can reduce the lifetime maintenance costs to a great extent. The actual performance of a CMS, needed to break-even, can be considered low, as the condition monitoring costs are in no proportion to the total gearbox maintenance costs and thus the potential revenue of the CMS.

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9 v Table of Contents Summary iii Table of Figures ix Table of Tables xi Table of Equations xiii Nomenclature xv Acknowledgements xvii Chapter 1. Introduction Problem and research questions Scope and current status 2 Chapter 2. Gearbox availability Failure rate Gearbox share in wind turbine failure rate Gearbox failure causes Gearbox functionality Gearbox failure modes Gearbox failure mechanisms Downtime Gearbox share in wind turbine downtime Gearbox downtime breakdown Stock components Wind farm distance Transport systems Weather window Repair time 18 Chapter 3. Gearbox maintenance costs Corrective maintenance Gearbox replacement costs Cost breakdown of an offshore gearbox replacement Preventive maintenance Scheduled maintenance Condition based maintenance Scheduled inspection visits Condition monitoring systems 26 Chapter 4. Cost model Monte Carlo method 33

10 vi 4.2 Gearbox maintenance categories Catastrophic gearbox failure Large gearbox failure Small gearbox failure Service and inspection visit Integration of failure probability Integration of CMS Reduce number of large and catastrophic failures Give an early warning to reduce waiting downtime 41 Chapter 5. Sensitivity analysis and CMS profitability Base case: near shore 6MW WT Without CMS With CMS CMS implemented to reduce large and catastrophic gearbox failures CMS implemented to reduce waiting downtime Combined benefit of CMS functions Influence of the distance to shore Without CMS With CMS CMS implemented to reduce large and catastrophic gearbox failures CMS implemented to reduce waiting downtime Combined benefit of CMS functions Influence of the gearbox failure rate Without CMS With CMS CMS implemented to reduce large and catastrophic gearbox failures CMS implemented to reduce waiting downtime Combined benefit of CMS functions 64 Chapter 6. Conclusions and discussion Conclusions Discussion 68 Bibliography 69 Appendices 77 A. European inflation rates 77 B. Maintenance costs estimation equations 79 B.1. Total gearbox replacement costs 79 B.2. Component costs 79

11 vii B.3. Labour costs 79 B.4. Access costs 79 B.5. Production loss 79 C. M-files 81 C.1. Main file 81 C.2. Repair costs file 84 C.3. Input file 85 D. Mean, standard deviation and Weibull parameters 87 D.1. Distance to shore: 87 D.2. Failure rate 87 D.3. For different percentages of probability reduction of large and catastrophic failures 88 D.4. For different values of prediction time 89 E. Commercial Wind Turbine CMS for the gearbox 91 F. Probability of failure for different failure rates 95 F.1. Probability of a gearbox replacement total loss 95 F.2. Probability of a gearbox replacement, which can be refurbished in workshop 95 F.3. Probability of a large gearbox failure 95 F.4. Probability of a small gearbox failure 95

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13 ix Table of Figures Figure 1-1 Installed Global Wind Capacity (GW) [1] 1 Figure 2-1 Wind turbine state with respect to time [15] 5 Figure 2-2 Bathtub curve [19] 6 Figure 2-3 Failure rate per component 7 Figure 2-4 Schematic of a parallel gear stage connection [24] 8 Figure 2-5 Schematic of a planetary gear stage [26] 8 Figure 2-6 Schematic of a three stage gearbox reproduced from [27] 9 Figure 2-7 Distribution of bearing failure causes as found by SKF [33] 11 Figure 2-8 Downtime per component 13 Figure 2-9 Example of a CTV [41] 14 Figure 2-10 Example of a helicopter used for crew transport to a wind turbine [43] 15 Figure 2-11 Example of a lift boat [45] 15 Figure 2-12 Example of a jack-up barge [46] 16 Figure 2-13 Example of a Self-Propelled Installation Vessel [47] 16 Figure 2-14 Example of a weather window distribution [40] 18 Figure 3-1 Maintenance concepts of a wind turbine [15] 19 Figure 3-2 Condition based maintenance compared to corrective maintenance [50] 21 Figure 3-3 Gearbox replacement costs with respect the wind turbine size 23 Figure 3-4 Cost breakdown gearbox replacement (based on [51] [54] and Appendix B) 24 Figure 3-5 Profit CMS by reducing the number of wind turbine visits from two visits a year 32 Figure 3-6 Profit CMS by reducing the number of visits per year from one visit a year 32 Figure 4-1 Schematic representation of the cost model, in which the red part represents the MC simulation 34 Figure 4-2 Schematic representation of the implementation of CMS performance parameters and 40 Figure 4-3 Cost-effective scheduling by CMS in case of: Top: A prevented large or catastrophic gearbox failure by a small repair action. Waiting downtime is prevented, because > Middle: A prevented large or catastrophic gearbox failure by a small repair action. Waiting downtime is not entirely prevented, because < Bottom: A large or catastrophic gearbox failure. Waiting downtime is prevented, because > 42 Figure 4-4 Variability of MC results as N is decreased from a single simulation for N= (middle red line) to 10 simulations with N= (blue lines) 44 Figure 5-1 Weibull distributions of the lifetime maintenance costs of a 6 MW wind turbine gearbox near shore for three different failure rate partitions 46 Figure 5-2 Weibull distribution of gearbox maintenance costs as a function of for the medium failure rate partition 47 Figure 5-3 CMS profit as a function of 48 Figure 5-4 CMS profit for different CMS costs as a function of for the robust failure rate partition 49 Figure 5-5 Monte Carlo simulations for gearbox maintenance costs and different values of for the medium failure rate partition 50 Figure 5-6 CMS profit as a function of to 10 days 51 Figure 5-7 CMS profit as a function of to 98 days 52 Figure 5-8 CMS profit for different CMS costs as a function of (Robust) 52 Figure 5-9 Lifetime gearbox maintenance costs for different as a function of for the robust failure rate partition 54

14 x Figure 5-10 Lifetime gearbox maintenance costs for different as a function of for the robust failure rate partition 54 Figure 5-11 Wind farms online, under construction and consented at different distances to shore [86] 55 Figure 5-12 Weibull distribution of the gearbox maintenance costs for different distances offshore 56 Figure 5-13 CMS performance requirements of to become profitable 57 Figure 5-14 Profit sensitivity to distance to shore as a function of 58 Figure 5-15 CMS performance requirements of to become profitable 58 Figure 5-16 CMS profit as a function of 59 Figure 5-17 Lifetime gearbox maintenance costs for a 6MW wind turbine near shore and far offshore for different values of as a function of 60 Figure 5-18 Lifetime gearbox maintenance costs for a 6MW wind turbine near shore and far offshore for different values of as a function of 60 Figure 5-19 Weibull distribution of the gearbox maintenance costs for different failure rates (Medium) 61 Figure 5-20 CMS performance requirements of to become profitable for different failure rates 62 Figure 5-21 Profit sensitivity to failure rate as a function of 63 Figure 5-22 CMS performance requirements of to become profitable 63 Figure 5-23 Profit sensitivity to failure rate as a function of 64 Figure 5-24 Lifetime maintenance costs for a wind turbine gearbox with a failure rate of 0.05 (solid lines) and 0.15 (dotted line) for different values of as a function of 65 Figure 5-25 Lifetime maintenance costs for a wind turbine gearbox with a failure rate of 0.05 (solid lines) and 0.15 (dotted line) for different values of as a function of 65

15 xi Table of Tables Table 2-1 Top 13 Failure modes of a wind turbine gearbox population listed in terms of failure costs by van Hornbeek et al. (2012) [28] 10 Table 2-2 Causes of gearbox failures by Ribrant et al. (2002) [7] 10 Table 2-3 Summary table transport systems 17 Table 3-1 Cost estimations of a catastrophic gearbox failure by different papers 23 Table 3-2 CMS capacity as found by van Horenbeek et al.[28] 30 Table 3-3 Lifetime maintenance costs for different scheduled inspection visits a year 31 Table 4-1 Estimated parameters for each failure category for a 6 MW wind turbine 30 km offshore 37 Table 4-2 MC model working principle with respect to number of F 1 failures over the wind turbine lifetime 38 Table 4-3 Estimated parameters downtime in days 43 Table 5-1 Results of Monte Carlo Simulation for the standard situation 45 Table 5-2 MC results for gearbox maintenance costs with a CMS system for different values of 47 Table 5-3 Mean profit for different values of 48 Table 5-4 Gearbox maintenance costs without and with a CMS system for different values of 50 Table 5-5 Mean profit for different prediction times, 50 Table 5-6 Lifetime maintenance costs for varying CMS performances for the weak failure rate partition 53 Table 5-7 Lifetime maintenance costs for varying CMS performances for the medium failure rate partition 53 Table 5-8 Lifetime maintenance costs for varying CMS performances for the robust failure rate partition 53 Table 5-9 Results of Monte Carlo Simulation for wind turbines at different distances offshore 56 Table 5-10 Results of Monte Carlo Simulation for wind turbines with different annual failure rates 61 Table 20 Inflation rates 77 Table D-1 Weibull parameters, standard deviation and mean of the gearbox lifetime costs for different failure partitions 87 Table D-2 Weibull parameters, standard deviation and mean of the gearbox lifetime costs for different failure partitions and different gearbox failure rates 87 Table D-3 Weibull parameters, standard deviation and mean of the gearbox lifetime costs for different CMS performances with respect to reduced probability of large and catastrophic failures 88 Table D-4 Weibull parameters, standard deviation and mean of the gearbox lifetime costs for different failure partitions and CMS performance with respect to prediction time 89 Table E-1 Survey of Commercial Wind Turbine CMS for the gearbox found by Yang et al. (Yang, Tavner, Crabtree, Feng, & Qiu, 2012) 91 Table F-1 Probability of failure F 1 for different failure rates and different failure rate partition categories 95 Table F-2 Probability of failure F 1 for different failure rates and different failure rate partition categories 95 Table F-3 Probability of failure F 2 for different failure rates and different failure rate partition categories 95

16 xii Table F-4 Probability of failure F 3 for different failure rates and different failure rate partition categories 95

17 xiii Table of Equations Equation 2-1 Technical availability [15] 5 Equation 2-2 Gear ratio calculation by number of gear teeth and rotational speed 8 Equation 3-1 Costs of a gearbox replacement offshore as a function of wind turbine power rating 23 Equation 4-1 Cost estimation of the lifetime gearbox maintenance for one wind turbine offshore 35 Equation 4-2 Probability of a catastrophic gearbox failure per year 35 Equation 4-3 Probability of a catastrophic gearbox failure per year, which can be refurbished for a next event 35 Equation 4-4 Probability of a large gearbox failure per year 36 Equation 4-5 Probability of a small gearbox failure per year 36 Equation 4-6 Simulation of component costs 38 Equation 4-7 also holds and is used for the simulation of the component costs of a large and small gearbox failure, between their own cost boundaries. 38 Equation 4-8 Mean gearbox maintenance costs of simulations 39 Equation 4-9 Standard deviation of the lifetime gearbox maintenance costs 39 The Weibull distribution is given by a two-parameter Weibull function as given by Equation 4-10: 39 Equation 4-11 Two parameter Weibull distribution function 39 Equation 4-12 Implementation of for the probability of a gearbox replacement with a total loss 40 Equation 4-13 Implementation of for the probability of a small gearbox failure 40 Equation 4-14 Condition a CMS has to fulfill to prevent waiting downtime for a small repair action 41 Equation 4-15 Condition a CMS has to fulfill to prevent waiting downtime for a large or catastrophic repair 43 Equation 16 Implementation of in the downtime costs calculation per failure 43 Equation 17 Waiting downtime 44 Equation B-1 Total gearbox maintenance costs 79 Equation B-2 Estimation of component costs 79 Equation B-3 Estimation of labour costs 79 Equation B-4 Estimation of access costs 79 Equation B-5 Estimation of costs made by production losses 79

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19 xv Nomenclature Latin symbols a Weibull scale factor [-] a i Number of catastrophic gearbox failures in wind turbine lifetime, total loss for simulation i [-] b Weibull shape factor [-] b i Number of catastrophic gearbox failures in wind turbine lifetime, refurbishment possible for simulation i [-] c i Number of large gearbox failures in wind turbine lifetime for simulation i [-] C i Total costs of i [ ] d i Number of small gearbox failures in wind turbine lifetime for simulation i [-] e i Number of scheduled inspection visits in wind turbine lifetime for simulation i [-] F i Represents maintenance event i [-] i Mean total gearbox maintenance costs of i [ ] N i Number of i [-] P i Probability of occurrence of i [%] p i Price of i [ ] R Gear ratio [-] S Represents an inspection visit [-] S wt Power rating wind turbine [MW] x Weibull function lifetime gearbox maintenance costs input [ ] y Weibull function probability output [-] Greek symbols ϐ gearbox Gearbox CAPEX share [%] Prediction time before functional failure or forced shutdown [days] Reduced probability of large and catastrophic failures by CMS [%] σ i Standard deviation of i [ ] ω i Rotational speed [rpm] Abbreviations AE BEP CBM CMS CoE CTV Acoustic Emission Break-Even Point Condition Based Maintenance Condition Monitoring System Cost of Energy Crew Transporting Vessel

20 xvi DOWEC ECN EWEA FR kw, MW, GW IEC LCC LWK MC Mt MTTF MTTR MW O&M OWEZ SCADA SKF SPIV SWPP TCP/IP WLAN WMEP WW Dutch Offshore Wind Energy Converter Energie Centrum Nederland (Energy Centre Netherlands) European Wind Energy Association Failure Rate Kilo-, Mega-, Gigawatt Information-Education-Communication Life Cycle Costs LandWirtschaftsKammer Monte Carlo Megatons Mean Time To Failure Mean Time To Repair Megawatt Operation and Maintenance Offshore Wind park Egmond aan Zee Supervisory Control and Data Acquisition Svenska Kullagerfabriken (Swedish Ball bearing Factory) Self-Propelling Installation Vessel Swedish Wind Power Plants Transmission Control Protocol/Internet Protocol Wireless Local Area Network Scientific Measurement and Evaluation Program Weather Window

21 xvii Acknowledgements It would not have been possible to write this thesis without the help and support of the kind people around me, to only some of whom it is possible to give particular mention here. First of all I would like to thank my thesis supervisors Claudia Hofemann and Cyril Boussion. Without their help, support, knowledge and advice this thesis would not have been possible. Their weekly guidance and critical view on my work were essential in the successful completion of my master thesis. Secondly I thank the judging committee, prof. dr. van Bussel, dr. ir. Arts and dr. ir. Zaaijer for their critical and interested view on my thesis. I am grateful for the responses I received from industry, especially the very informative and quick responses from Bart Hoefakker, Managing Director of NoordzeeWind and Franklin Heinsen, Business Manager Condition Monitoring of SKF. I would like to acknowledge the help of the University of Delft, especially the department of Wind Energy which provided the necessary support for this research. The library and computer facilities of the University, as well as the free coffee and lunch conversations, have been indispensable. Amongst my fellow graduate students in the Department of Wind energy, in promoting a stimulating and welcoming academic and social environment will stand as an example to those that succeed them. Last, but by no means least, I thank my family and friends for their support and encouragement in completion of the studies and the project in particular.

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23 I dedicate this thesis to my late father, who is still an inspirational and motivating factor in my life. xix

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25 1 Chapter 1. Introduction 1.1 Problem and research questions During the past decades, great efforts have been undertaken to make wind power a viable and competitive source of electricity. The share in global energy production has grown extensively. In the end of 2012 global installed wind capacity has risen to 282 GW, almost a tenfold of the capacity in 2002 [1], as depicted in Figure Installed global Wind Capacity (GW) Annual Cumulative 50 0 Figure 1-1 Installed Global Wind Capacity (GW) [1] To keep up this pace in growth and to account for decreasing government support in the future the cost of energy (CoE) of wind energy, especially offshore, will have to decrease. The high costs of wind energy are for a large part a result of the high investment costs [2] [3]. The installation, cable laying and fabrication of the offshore wind turbines are the main contributors to these costs. However a significant part of the costs are made during operations, distributed over the lifetime of approximately 20 years. Over the entire wind turbine lifetime operation & maintenance (O&M) represents a significant cost share [3] [4], in particular offshore, where service is difficult and expensive [5]. O&M refers to all activities performed after a wind turbine has been commissioned in order to have and keep the turbine in operation. Operational costs are a result of the day-to-day tasks involved in a wind farm, such as scheduling site personnel, monitoring turbine operation, responding to turbine fault events and coordinating with the utility to address curtailment or outage issues [4]. Maintenance is the combination of all technical and associated administrative actions intended to retain an item or system in, or restore it to, a state in which it can perform its required function [6].Maintenance costs are the dominant factor of total O&M costs, thus if the cost of energy have to be decreased significantly the maintenance costs are one of the primary targets.

26 2 To decrease the maintenance costs for offshore wind farms, Condition monitoring systems (CMSs) could be an answer. A CMS continuously monitors the performance of wind turbine parts and helps determine the optimal time for maintenance [5]. The gearbox is chosen, due to the high downtime, the related high maintenance costs and its suitability for CMSs. In order to decrease the maintenance costs, a CMS installed on the wind turbine gearbox should become profitable after its lifetime. This means that the CMS should have a needed performance, quantified in this thesis. As gearbox maintenance costs are affected by many variables, sensitivity analyses are performed in combination with a CMS cost-benefit analyses. The research question is: What is the required performance of CMSs to be profitably installed at a gearbox of an offshore wind turbine? The research question is divided into three sub-research questions: Why focus on gearbox maintenance as the most significant way to decrease the overall maintenance costs? What does offshore gearbox maintenance comprehend and to what extend are its costs affected by different variables? How can the performance of a CMS be measured and what can a CMS do for the lifetime maintenance costs of the gearbox? Chapter 2 answers the first sub-research question, by giving a detailed view on the gearbox availability, which is affected by failure rate and downtime. The downtime and failure rate are discussed on both turbine and component level. In order to increase the gearbox availability proper maintenance has to be applied. Different forms of maintenance are described in Chapter 3. For both corrective and preventive maintenance, the costs of a maintenance action are quantified in order to estimate the lifetime gearbox maintenance costs in Chapter 5. Chapter 5 describes the results of a sensitivity analysis, which estimates the lifetime gearbox maintenance costs, while varying different parameters. Subsequently the effects of a CMS are analysed in a cost-benefit analysis and the minimum CMS performance to be profitably installed are estimated. The model used for these analyses is explained in Chapter 4. The thesis is finalized by a conclusion and recommendations for further research in Chapter Scope and current status The performance of condition monitoring systems for offshore wind turbines with respect to gearbox maintenance costs is not clearly defined yet. However there is an abundance of papers, discussing the availability of wind turbines, as well as their condition monitoring systems. First of all the availability of wind turbine components is described in different reports. German, Danish, Swedish and Finnish wind turbines have been monitored for several years, reporting failure rates and downtimes [7]-[9]. Those reports enable to assess the importance

27 3 of CMSs and note that a CMS will be more profitable for components with high failure rates and downtime. Rademakers et al. [10] used cost models of TU Delft and ECN Wind Energy to explore the factors that influence the O&M. The economic justification of condition monitoring with respect to operational parameters for a wind farm is described by McMillan and Ault [11]. Their results show that the levels of benefit are dependent on a variety of factors including wind profile, typical downtime duration and wind turbine sub-component replacement costs. Secondly they claim that for a CMS to be cost-effective an accurate diagnosis in around 60% to 80% of cases has to be provided, depending on the costs of the maintenance actions. Besnard [12] performed life cycle analysis for the cost-benefit of health monitoring systems for wind turbines. Here different maintenance strategies are compared with and without condition monitoring systems using an economic model, containing the effectiveness of the on-line health monitoring system. The work of Nillson and Bertling presents a life cycle cost (LCC) analysis with strategies where CMSs improve maintenance planning for a single wind turbine onshore and a wind farm offshore [5]. The main conclusion of the paper is that a decrease in corrective maintenance, i.e. prevention of failures, is needed to justify the CMS: the author concludes that the availability needs to increase by 0.43% annually to cover the CMS costs. Alternatively, 45% of corrective maintenance needs to be displaced by preventative maintenance. Van Horenbeek et al. [13] did find a relationship between gearbox maintenance costs and CMS efficiency. They analysed the cost benefits of a CMS for an onshore wind turbine population. They did though use Monte Carlo simulations to estimate the lifetime costs, as this research will do. Different to their research, this work focuses on offshore wind turbines and the sensitivity of wind farm distance and failure rate to both gearbox maintenance costs and CMS performance.

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29 5 Chapter 2. Gearbox availability A proper O&M strategy is important to achieve a high availability of the wind farm. Availability can be defined as: the proportion of time the equipment is able to perform its function [14]. Figure 2-1 and Equation 2-1 show that the availability is influenced by the time it takes to repair or service a system (downtime) and the number of failures that occur per unit of time (failure rate). Figure 2-1 Wind turbine state with respect to time [15] Equation 2-1 Technical availability [15] A wind turbine thus is available when the blades are technically able to rotate and electricity can be produced. The availability therefore is independent of the wind conditions at the site. Failing of a component means that service is required and the wind turbine cannot function for a certain period of time. The downtime is the time that a component is not functioning after a failure has occurred. This chapter reveals that not the gearbox, but the electric systems, electric control, sensors and the hydraulic systems represent the dominant components when it comes to failure rate. Different studies show that the gearbox represents between 4.6% and 9.8% of the total wind turbine failures. Bearing failures followed by gear, shaft and lubrication system failures, causes a majority of these failures. The high number of bearing failures is a result of poor fitting, by poor lubrication, contamination or fatigue. Unlike for the failure rate, the gearbox is the dominant component when it comes down to downtime per failure. The downtime depends on several factors including, spare part availability, wind farm distance, transport system, weather window and repair time.

30 6 While the failure rate of the gearbox is less than most components, the downtime is significantly higher and so are the repair costs. Repairs or replacements within the operational lifetime have a significant impact on the operational economics of the wind turbine. Besides the revenue loss due to long downtime, high replacement costs and complex repair procedures are the major reasons for concerns about gearbox reliability [16]. Secondary damage from a relatively cheap bearing failure could lead to the replacement of the whole gearbox, which leads to enormous repair costs as a jack-up barge has to be hired, a new gearbox shipped in and the lost revenue of electricity production [17] [18]. Especially offshore the maintenance costs are high, as illustrated in Chapter Failure rate Many products in industry are following a similar trend of failure rate in time. This typical distribution is presented in Figure 2-2, where failure rate is plotted in relation to the product lifetime. This is called the Bathtub-curve. Figure 2-2 Bathtub curve [19] The Bathtub-curve shows high failure rates in the first time period, the early failures. These are often explained by manufacturing, usage or design flaws, which come to surface in an early stage [19]. These early failures are generally followed by a longer more stable period of random failures, which can occur at any time of the product s lifetime [20]. In the final phase of the product lifetime wear and damage accumulation results in wear-out failures, resulting in higher failure rates.

31 Failure rate (%) Gearbox share in wind turbine failure rate Only few failure data is available to investigate wind turbine component failure rate. However studies in Germany (LWK [9] and WMEP [21]) and in Sweden and Finland [7], give an indication of what components are the most sensitive regarding to failure. Figure 2-3 is based on the information presented in these studies. LWK, standing for LandWirtschaftsKammer, performed a survey on a population up to 650 wind turbines of known model and design in Schleswig Holstein in Germany from [9].Secondly the Scientific Measurement and Evaluation Program (WMEP) was performed by ISET and carried out on behalf of the German Ministry for the Environment, Nature Conservation and Nuclear Safety. It contains data from over wind turbines in different regions of Germany over a period of 15 years ( ) [8]. In addition the Swedish survey, performed by Ribrant and Bertling, collected data of failures of 723 wind turbines for the period of 2000 and 2004 [7] Failure rate per component SWPP LWK WMEP Figure 2-3 Failure rate per component Figure 2-3 shows that the electrical system, sensors and the control system are the weakest spots in the wind turbine. They comprise respectively between 30% and 51% of all failures. The gearbox failure rate is not amongst the highest, with a share in wind turbine failures ranging from 4.6% to 9.8%. However the gearbox accounts for a remarkable share of the overall maintenance costs, caused by significant downtime and expensive repairs, as will become clear in later sections of this report Gearbox failure causes In order to find out where the gearbox failures are coming from, gearbox failures are analysed in more detail. First the working principle of a standard wind turbine gearbox is given. The second part of this section comprehends the main gearbox failure modes, as well as the failure mechanisms that are presumably causing these failure modes.

32 Gearbox functionality A gearbox is an important part in the drive train of a wind turbine. It is used to transfer the wind powers kinetic energy from the low speed shaft to the high-speed shaft. In this way a rather compact generator system can transform the kinetic energy into electricity. Wind turbine designs without gearbox exists today as well, the so-called Direct Drive wind turbines. However as Direct Drive systems weigh around 25% as much and costs around 30% more, it is not likely that the gearbox will vanish completely in the near future [22]. In order to gear up the rotational speed, multiple stages are used. In general a wind turbine gearbox consists of [23]: 1. one planetary stage (low speed) supported by a full complement support bearing, 2. one or more parallel stages supported by a cylindrical roller bearing. The parallel stages are simple and consist out of two gears of different size in contact to each other (Figure 2-4). Figure 2-4 Schematic of a parallel gear stage connection [24] The ratio, R, in number of gear teeth, N i, result in the ratio of each shaft rotational speed, ω, according to: Equation 2-2 Gear ratio calculation by number of gear teeth and rotational speed The planetary stage is more complex. It consists out of a sun gear in the middle, which is in case of a wind turbine connected to the low speed shaft (Figure 2-5). Three planet gears are placed around this sun gear and are held in place by a surrounding planet ring. Such systems are compact and reduce the overall gearbox weight, because three planet gears are at all times in contact with the sun gear and therewith equally share the stresses and forces. As a result net radial forces are prevented. The complexity of this design could however make it a more vulnerable component [25]. Figure 2-5 Schematic of a planetary gear stage [26]

33 9 A schematic of a three-stage gearbox, consisting of one planetary gear stage, with a medium speed and a high-speed parallel stage, is shown in Figure 2-6. Figure 2-6 Schematic of a three stage gearbox reproduced from [27] Both the gear stages and the bearings are part of the gearbox and need to be lubricated continuously. The lubrication serves several reasons [23], such as: to reduce friction and wear by introducing a lubricating film between moving parts, cooling to dissipate heat away from the critical parts of the equipment, cleaning and suspending, to facilitate smooth operation of equipment by removing and suspending products, such as carbon, sludge and varnish, protection to prevent metal damage due to oxidation and corrosion. Insufficient or contaminated lubrication can cause serious damage to the gearbox. The different failure modes and failure mechanisms are briefly described in the following sections Gearbox failure modes In the study by van Hornbeek et al. [28] a top 13 of possible failure modes based on the related costs is established, whereas the first 6 gearbox failure modes (Table 2-1) seem to account for 80% of the total estimated gearbox failure costs.

34 10 Table 2-1 Top 13 Failure modes of a wind turbine gearbox population listed in terms of failure costs by van Hornbeek et al. (2012) [28] Failure mode Share of total failure costs 1. High speed shaft bearing failure 27,8% 2. Broken intermediate shaft 21,2% 3. Intermediate shaft bearing failure 10,1% 4. Planet bearing failure 9,6% 5. Broken center post 6,2% 6. High speed shaft bearing black spot 5,4% 7. Sun gear Broken teeth 5,3% 8. Low speed shaft bearing failure 5,0% 9. Intermediate shaft bearing failure 4,8% 10. High speed shaft grinding temper failure 2,3% 11. Broken low speed wheel 1,2% 12. Oil pump failure 0,8% 13. Intermediate shaft splash plate failure 0,2% Ribrant et al. [7], analysed the cause of the gearbox failures based on data from 723 wind turbines, located in Sweden. Table 2-2 shows the distribution of failures. Half of the failures were caused by the bearings. Table 2-2 shows that the majority of the failures are caused by wear (B1). Besides the bearing failures the gearwheels represent the highest downtime of the gearbox components. Component Table 2-2 Causes of gearbox failures by Ribrant et al. (2002) [7] Number of failures Average downtime (hours) Number of failures, Cause: B1 Average downtime, Cause B1 (hours) Bearings Gearwheels Shaft Sealing Oil system Not specified In line with Table 2-1 and Table 2-2 and in studies by Sankar et al., Needelman et al. and Musial et al. there is consensus that bearing failures are the main cause of gearbox failures [18], [29]- [31]. McNiff [32] describes that the planet bearings, the intermediate shaft-locating bearings and high-speed locating bearings tend to fail at the fastest rate, while the planet carrier bearings, hollow shaft bearings and non-locating bearings are most unlikely to fail. According to SKF [33], a manufacturer and supplier of ball and roller bearings, the main causes of bearing failures are caused by poor fitting, by poor lubrication, by contamination of both the bearing and the lubrication oil and by fatigue as machines are overloaded, incorrectly serviced or neglected. This is graphically presented in Figure 2-7.

35 11 16% 34% 14% poor fitting poor lubrication contamination fatigue 36% Figure 2-7 Distribution of bearing failure causes as found by SKF [33] Though bearing failures are dominant, other failure modes have to be looked at as well. As mentioned in the lubrication systems serves important features for the gearbox. According to Fischer et al. [34] the dominant failures of the lubrication system are too high oil temperatures, which results in insufficiently thin oil films. This is caused by a failure of the temperature control system or more seldom by a defect in the cooling system. Second failure of the lubrication system is too low oil temperature, which in that case has a too high viscosity to ensure appropriate lubrication. In most cases this is caused by a failure of the gearbox oil-heating system or by non-homogeneous heating and insufficient mixing of the oil. Finally gearwheel failures are briefly discussed. The gearwheels transmit the mechanical power, while converting speed and torque to the desired ratio. The main gearwheel failures are caused by excessive wear or breakage [34]-[36]. A detailed look at the underlying causes for the failure modes of both the gearbox bearings and the gearwheels are given in the next section Gearbox failure mechanisms In this section the most frequently encountered failure mechanisms of a wind turbine gearbox are mentioned and explained briefly. These failure mechanisms are causing the bearing and gearwheel failures, as describe before. The most frequently encountered bearing failure mechanisms, as found in a NREL research [36], are cracking, abrasion and scuffing. Fretting corrosion and bending fatigue can be considered to be the cause of the main failures of the gears in a gearbox [36]. Each mechanism is briefly described in this section. A better understanding of these failures could be important for the choice and profitable implementation of a CMS.

36 Cracking Cracking generally is an inter-granular procedure, with the crack running from the surface of a roller or ring towards its centre of mass in a relatively straight line. This can be caused by insufficient lubrication, alternating loads, heavy loads or fatigue rupture [36] Abrasion Abrasion can occur by embedded particles of one bearing surface to the opposing bearing surface (two-body abrasion) or by loose contaminants, presumably transported in by the lubrication oil (three-body abrasion). Abrasion scratches on bearing surface are in the direction of sliding. Under magnification scratches appear as parallel furrows that are smooth and clean. Abrasion is usually caused by contaminants in the lubrication fluids by hard, sharp-edged particles. Common contaminants are sand, machinery chips, grinding dust, weld splatter and wear debris [36] Scuffing Scuffing is the transfer of material from one bearing surface to another due to welding and tearing. Damage typically occurs in areas of slip in narrow or broad bands along the direction of sliding. It may occur in localized patches with load concentrations. Often it is a result of insufficient lubrication. When the oil film thickness is less than the composite roughness of the pinion and gear, metal-to-metal contact occurs. Scuffing areas appear to have a rough or matte texture, which under magnetization appears to be torn or plastically deformed [36] Fretting corrosion A deterioration of contacting gear tooth caused by minute vibratory motion is called fretting. It occurs between contacting surfaces that are pressed together and subjected to cyclic, relative motion of extremely small amplitude. Under these conditions, lubricant is inadequate to replenish, permitting metal-to-metal contact and causing adhesion of surface asperities [36] Bending fatigue According to Sheng et al. [36] bending fatigue can be subdivided into three stages: crack initiation, propagation and fracture. High-cyclic bending fatigue occurs when cyclic stress is less than the yield strength of the material and the number of cycles to failure is greater than Crack initiation is the stage where no gross yielding of the teeth occurs. However local plastic formation may occur in regions of stress concentrations or areas of discontinuities. The end of this stage is symbolized by the formation of micro cracks inside grains. On the moment the crack is propagating through the grain boundaries, the failure is in the second stage. The crack normally propagates in the direction of the highest tensile stress. Plastic deformation is confined to a small zone at the loading edge of the crack. As a result the cracked surface usually appears smooth without signs of gross plastic deformation [36]. A sudden fracture occurs in the final stage and corrective maintenance has to be performed. 2.2 Downtime Besides the failure rate, the downtime of a component after failure is an important parameter for the availability of a component. The downtime is the time that a component is not functioning after a failure has occurred.

37 Downtime percentage per failure Gearbox share in wind turbine downtime The duration of downtime can be influenced by a number of factors, such as the component of question. As can be seen in Figure 2-8, the average downtime differs significantly between the different components. 25 Downtime due to component failure LWK WMEP Figure 2-8 Downtime per component 1 What is interesting in Figure 2-8 is that the components that were dominant in failure rate distribution (Figure 2-3), have relatively low percentages of downtime per failure. The figure reveals that one component is more time demanding to repair or service than another. For instance the failure of an electric system is often to be solved by simply resetting the system. This means minimal downtime, where on the contrary the replacement of a component for the gearbox or generator demands a maintenance crew to visit the wind farm and replace the component, which can take up to several days. Figure 2-8 depicts that the gearbox failures result in relatively high downtime. Therewith it has very high cost of repair as well [37]. The duration of downtime, caused by malfunctions, depends on several factors. These are briefly described in Gearbox downtime breakdown Gearbox downtime is influenced by a number of factors. In this section, the main factors are briefly described. Main factors that influence the length of downtime are: spare parts availability: some repair actions need to replace specific component parts, whether or not these parts are already in stock or needs to be ordered and transported results in different time delays, distance to wind farm, transport system, availability of wind farm (weather and wave conditions), repair or service action at the wind turbine. 1 In this figure the data of the Swedish survey is not included since they did not publish data of downtime per failure in their work

38 Stock components The non-availability of small stock components should not cause significant time delays. These small parts are normally part of a back-up stock or can be ordered within short time. The availability of stock components can be a problem if a catastrophic failure occurs. Such a failure could result in the replacement of the entire gearbox. These are not always available in the stock supply, resulting in a new order to the manufacturer, which can last up to several weeks Wind farm distance The distance of the wind farm influences the time it takes to get to the wind farm for the maintenance crew. In this thesis the focus is solely on offshore wind farms, so it is easy to understand that the transport time is significantly shorter for an offshore wind farm 20 km of the coast, compared to a wind farm 100 km out of the coast. The traveling time is not only a function of the wind farm distance, but depends on the transport system and the weather conditions as well. According to EWEA the average distance of offshore wind farms was 29 km in 2012, almost 24% more than in 2011 (23,4 km). Looking at projects under construction, consented or planned, it is clear that distances to shore are likely to increase further, with projects announced up to 200 km from shore [38] Transport systems A variety of transport systems are used for the maintenance of wind farms. The system that is used depends on different aspects, especially the goal of the wind farm visit. If only personal has to be transported, no need of a large crane ship or Jack-up barge is needed and a Crew Transporting Vessel (CTV) or helicopter can be used. However if a large failure has occurred and a big component has to be replaced the helicopter or CTV does not suffice anymore CTV (Crew Transporting Vessel) A Crew Transporting Vessel (CTV) is used for the transportation of personnel or transfer of small loads (<1Mt) on the wind turbine platform. It is a high speed vessel that can reach 40 km/h [39] and can operate under weather conditions with wave height up to 2.5 m and wind speed of 54 km/h. Estimated costs are 1000,- to 2.000,- a day [40]. Figure 2-9 Example of a CTV [41]

39 Helicopter If only personal has to be transported to the wind turbine, the wind farm operator can choose to use a helicopter. The helicopter is not constrained by a significant wave height. It is a high-speed transport system (250 km/h. [42]) that can operate until wind speeds of 61 km/h [39]. Compared to a CTV costs are significantly higher for a helicopter at an hourly rate of [42]. Therefore normally a helicopter will be used only for very high priority repairs or during rough sea conditions [40]. Secondly the wind turbine needs to be suitable for helicopter transport, by means of a helicopter or a passenger landing platform as in Figure The fact that the physical dropping off/recovering of crew members has to be done in daylight (visibility) does limit the amount of time available to work on the WT especially in winter [42]. Figure 2-10 Example of a helicopter used for crew transport to a wind turbine [43] Lift boat A lift boat (Figure 2-11) is able to transport and lift medium loads on the platform (<40 Mt). The lift boat has a travelling velocity up to approximately 15 m/s and can operate at a wave height of 2 m and wind speeds up to 54 km/h [39] [40]. Approximated costs vary between and a day [44]. Figure 2-11 Example of a lift boat [45] Jack-up barge A jack-up barge can be used to transport and lift the heaviest components of a wind turbine, such as the gearbox, nacelle and rotor. It has a travelling velocity between 5 and 10 km/hr.

40 16 And can travel up to wave heights of 2 m and wind speed of 54 km/h. It however requires the use of tugs to travel to its desired location. The hoisting operation is limited with respect to the wind speed (36 km/h) [40]. The high load lifting capacity of such a system is pricy with an estimated daily rate varying between and [44]. This includes the additional costs of tugs and CTVs. Figure 2-12 Example of a jack-up barge [46] Self-propelled installation vessel Self-Propelled Installation Vessels (SPIVs) (Figure 2-13) are able to transport and lift high loads at sea. Unlike a jack-up barge SPIVs are capable to travel on its own and with a faster travelling time. The price can vary from up to a day [44]. Figure 2-13 Example of a Self-Propelled Installation Vessel [47] Table 2-3 gives a summary of the properties of each transport system, mentioned above. Here the day rate is expressed in expected day rate as found by Kaiser et al. [44].

41 17 Table 2-3 Summary table transport systems Traveling speed [km/h] Max. wave conditions [m] Max. wind conditions [km/h] Expected day rate [ /day] Cargo and lifting capacity CTV Helicopter Lift Jack Up SPIV boat barge ? to 2000 Only personnel Only personnel < 40 Mt All components All components Weather window The downtime also depends on the accessibility of the site. The accessibility depends on the weather window [48]. The transport systems are subjected to strictly defined weather restrictions, as the safety of personal has to be guaranteed. A weather window is the time frame in which certain weather conditions are continuously fulfilled [49] and thus when the maintenance crew is able to visit and service the wind turbine. Depending on the weather restrictions, local weather conditions and season, operations can last up to two to three times longer when compared to weather independent counterparts [49]. There are significant differences in the weather windows for different operations at sea. Small vessels, that are only used to transport personal and light equipment are fast and can withstand and operate at relatively rough weather conditions. On the other side if large components have to be replaced, such as the gearbox, a jack up barge or SPIV is used. These vessels can only operate and lift at steady weather conditions, making the weather windows for lifting operations significantly rarer. This causes extra downtime of the turbines, and gives an extra reason of avoiding large gearbox failures. According to Rademakers et al. [48] the accessibility is hardly influenced if only access systems are needed for transferring personnel and small spare parts, which can operate at wave heights above 1.0 to 1.5 m and wind speeds above 28.8 to 36 km/h. The need of systems that can operate under less harsh weather conditions only, will drastically influence the availability. Examples of these systems are the heavy load vessels, needed for replacing large components such as the gearbox. Figure 2-14 gives an example of a weather window, where the blue lines at the bottom indicate the windows at which the wind farm can be visited and maintenance can be performed. The blue lines at the top indicate times of high wind and waves, which causes waiting downtime. 2 Is in per hour

42 18 Figure 2-14 Example of a weather window distribution [40] Repair time The actual repair time at the wind farm differs per failure. For the replacement of small parts the repair time takes only a few hours. However this can take up to 3 to 5 days in case of the replacement of large parts or the entire gearbox [40]. The repair time includes the let-down of the failed part and hoisting the replacement part to the nacelle.

43 19 Chapter 3. Gearbox maintenance costs As stated before, the O&M costs represent high costs over a wind turbine lifetime. The operational costs are a result of the day-to-day tasks involved in a wind farm. This involves scheduling site personnel, monitoring turbine operation and coordinating with the utility to address curtailment or outage issues [4]. Operations also include the on-going activities associated with inventory management, coordinating with sub-suppliers for site and maintenance services, administering power purchase agreements, and submitting and tracking warranty claims. Wind farms are equipped with a Supervisory Control and Data Acquisition (SCADA) system. It enables the operator to start, stop and reset individual machines from a central location. Control of power factor correction equipment is often integrated into the SCADA system [4]. Operational costs are relatively constant and easy to predict over the wind turbine lifetime. This, however, is not the case for the maintenance costs of a wind farm. Failures can occur at random moments within the turbine lifetime and therefore are hard to predict. A failure and the downtime of a turbine directly results in a lower availability, as visualised by Figure 2-1 and Equation 2-1 in Chapter 2. To achieve high availability, efficient maintenance is important. Two main types of maintenance can be distinguished for wind turbines as schematically presented by Figure 3-1. Maintenance Corrective maintenance (repair) Preventive maintenance (service) Batch wise corrective maintenance Corrective maintenance on demand Scheduled maintenance Condition based maintenance Figure 3-1 Maintenance concepts of a wind turbine [15] Corrective maintenance is performed after a failure occurred. Corrective maintenance causes high costs and large uncertainty in the lifetime maintenance costs. If a gearbox failure is not noticed in time, a relatively small failure could cause more severe secondary damage. In the worst case scenario this results in a necessary replacement of the entire gearbox, with dramatic costs as a consequence.

44 20 In order to prevent high cost failures and downtime, a lot of investments are being done in preventive maintenance. Preventive maintenance costs are generally less than corrective maintenance, as is the resulting downtime of the turbine [48]. The objective of preventive maintenance is to replace components and refurbish systems before failure occurs or, in case a failure cannot be prevented, to minimise repair costs. Preventive maintenance can be subdivided into scheduled maintenance and Condition Based Maintenance. Scheduled maintenance is performed by maintaining and replacing wind turbine components in accordance with an established time-schedule or established number of units used. An alternative way of preventive maintenance is condition based maintenance (CBM). CBM is performed after one or more indicators show that equipment is going to fail or that equipment performance is deteriorating. CBM can be performed by maintenance crews, which visually inspect the wind turbine once or twice a year in order to estimate the condition of components [2]. CMSs are able to do this, reducing the need of physically visiting the wind turbine. Sensors attached to wind turbine components are monitoring various parameters, which are subsequently analysed by a data-mining system that is able to detect anomalies or deterioration of the components. By the help of such online condition monitoring systems failures can be predicted and detected in a very early stage and therewith significantly reduce maintenance costs. A comparison of the two CBM maintenance strategies (CMS and inspection visits) and corrective maintenance with respect to wind turbine life time, as mentioned in this section, is given in Figure 3-2. Figure 3-2 indicates that condition based maintenance with CMSs could reduce the amount of wind farm visits, compared to the regular inspection visits. Secondly by the help of Condition Monitoring Systems severe failures of a component could be prevented, and with it the high costs and downtime of corrective maintenance. This is only the case when the CMS is used in the correct way and gives trustworthy predictions in time, followed by efficient servicing by specialised teams.

45 21 Figure 3-2 Condition based maintenance compared to corrective maintenance [50] In order to get a more detailed view what the maintenance categories and their costs comprehend, corrective maintenance and preventive maintenance are described in more detail in this chapter. 3.1 Corrective maintenance If a failure does occur corrective maintenance has to be applied. Corrective maintenance as the only maintenance strategy for wind turbines is risky, since failures of relatively small and dispensable components can lead to severe consequential damage (secondary damage). Secondly the failures of components are often a result of high loads. These loads act on the wind turbine during high wind speeds, which mean that a failure at that time results in high revenue losses as well. Thereby, for offshore wind turbines, the accessibility at that time is probably poor as well [50]. In order to estimate what corrective maintenance can cost in a worst case scenario, the costs for the replacement of the entire gearbox are analysed. The costs of such a gearbox replacement are estimated by a literature study. Various papers claim they know what a major gearbox replacement approximately costs [5], [40], [51] - [56]. These papers are used to get a general idea of the costs to replace a gearbox. Subsequently the dominant cost factors in this operation are analysed as well Gearbox replacement costs As there is no database publicly available with actual financial numbers in case of recent offshore gearbox replacements, the numbers found in Table 3-1 are estimations. In order to compare those numbers, it needs to be clear what the numbers exactly comprehend. 1) The replacement costs from Andrawus et al. [51] are estimated, based on an onshore wind farm consisting of kw wind turbines. Uncertainties in the financial calculations are risk assessed using a probabilistic technique of the Crystal Ball Monte Carlo simulation. As this paper focusses on an onshore wind farm the data is corrected, as the access costs and, to a lesser extent, the production losses offshore will be higher.

46 22 2) A research by Tegen et al. indicates a gearbox price of 137 $/kw (100,7 /kw) for a 1,5 MW baseline project in 2010 [52]. As this represents the costs of the gearbox component only, this is corrected with a factor 1.25, to find the total replacement costs of a 1,5 MW gearbox. The scale factor is based on the cost breakdown as described in ) This is an average of an estimate by Jon Pedersen, Site Manager for Rødsand II Wind Farm, where he claims that the replacement costs for a new gearbox would be around $ [56]. Rødsand II is a wind farm commissioned in 2010 and located 9 km outside the coast of Denmark with 90 2,3 MW wind turbines [57]. 4) Nilsson and Bertling [5] received gearbox replacement costs from an offshore 3 MW wind turbine out of first hand by interviewing the operating manager from Vattenfall in Sweden and the Project Team Manager from the offshore wind farm Kentish Flats located in the North sea. 5) During industry feedback it was stated that the cost for an offshore gearbox replacement amounted ) An NREL research by Fingersh et al. [55] gives a cost formula of each wind turbine component as a function of wind turbine rating. As this only includes the costs of the component, the total replacement costs are corrected with a factor of 1.25, based on the replacement cost breakdown as described in ) Ault et al. [53] estimation of the replacement costs of an offshore 5 MW wind turbine is calculated using the percentage of capital cost method proposed in [58]. Capital costs of 1 M /MW (1,207 M /MW) of installed capacity was assumed for the purpose of deriving replacement costs estimates. 8) A study by Rademakers and Braam [40] gives an estimation of the gearbox replacement costs for a 6 MW offshore wind turbine. This study already took place in 2002, in which the development of 6 MW turbines and large offshore wind farms was still in a very early stage. This could be one of the reasons the total replacement costs are unrealistically low. Therefore this value is not included in Figure ) A final cost estimation comes from an attempt to estimate the O&M costs for the DOWEC baseline [54], a fictive wind farm of 80 6 MW wind turbines. As the mentioned researches spring from different years, the found values are corrected with the inflation rates based on the Eurostat index 2013 [59]. These rates can be found in Appendix A. The found gearbox replacement costs are listed in Table 3-1. Subsequently in Figure 3-3 a trend is visualized, where an increase in wind turbine power rating results in an increase in gearbox replacement costs, with the exception of the 6 MW wind turbine analysed by Rademakers and Braam [40].

47 Gearbox replacement costs [ ] 23 Table 3-1 Cost estimations of a catastrophic gearbox failure by different papers Nr. Size Year of data Total replacement costs [ ] Inflation corrected [ ] 1 0, , ,94 2 1, , ,50 3 2, , , , , , , , , , , , , , , , , , , Wind turbine size [MW] Figure 3-3 Gearbox replacement costs with respect the wind turbine size The shape of the trend line is chosen as a power function, as this is prescribed by the scale function as found by Fingersh et al. [55] As the component cost is the dominant variable in the gearbox replacement costs (3.1.2) and the component cost increases with size as a power function, the choice of the power shaped trend line can be considered valid. The function of the trend line in Figure 3-3 is given by the following equation: Equation 3-1 Costs of a gearbox replacement offshore as a function of wind turbine power rating in which S WT represents the wind turbine rating in MW. It must be noted that there is quite a large uncertainty level in Table 3-1 and Figure 3-3. The replacement costs depend on variables that differ for each wind farm and wind turbine type, for example variables such as the gearbox type, wind farm distance, downtime etc.. Therefore Figure 3-3 can only be used to get an indication of the gearbox replacement costs. Secondly Figure 3-3 is based on estimations by other researches as well. A higher degree of certainty is acquired when this result is validated with more actual wind farm data, which is

48 Gearbox replacement costs breakdown 24 very hard to obtain. However for the remaining of this thesis, the trend found in Figure 3-3 has been used Cost breakdown of an offshore gearbox replacement. In the cost estimations by Andrawus et al. [51] and by the Dowec Baseline [54] cost breakdown can be found. This shows that the total replacement costs consist of component, labour, access/lifting costs and production loss. Both however describe a totally different wind farm. Andrawus et al. analysed an onshore wind farm with wind turbines of 600 kw. In the DOWEC baseline an offshore wind farm with 6 MW turbines is simulated. Nevertheless if we compare the cost breakdown as visualized in Figure 3-4 it is clear that the component costs are the dominant driver in the total gearbox replacement costs. In addition to these estimations, costs are estimated by this research as well. This adds confidence to the found data and provides better insight in the underlying cost variables. The cost calculations of an offshore gearbox replacement are a summation of component costs, labour costs, access costs and the costs of production loss. Details and equations can be found in Appendix B. Access/lifting costs are significantly higher for the 6 MW offshore wind turbines as is expected. An onshore lifting operation is relatively cheap as the crane costs only around a day [51], where a vessel that is able to lift a gearbox at sea can cost up to several hundreds of thousands euro [54]. The difference in production loss is caused by two factors. First a large wind turbine (here 6 MW) logically produces more power per unit of time than a small 600 kw wind turbine. In case of wind turbine downtime, this automatically leads to higher revenue losses per unit of time. Secondly wind turbines offshore are generally harder to access and therefore have longer downtimes, compared to wind turbines onshore. 80,0% 70,0% 60,0% 50,0% 40,0% 30,0% 20,0% 10,0% 0,0% 600 kw onshore by Andrawus et al. 600 kw onshore thesis estimate 6 MW offshore DOWEC baseline 6 MW offshore thesis estimate Figure 3-4 Cost breakdown gearbox replacement (based on [51] [54] and Appendix B)

49 25 In case gearbox replacements or large repairs can be prevented significant cost savings, such as high component costs, can be achieved. Secondly for the replacement of small parts there is no need for the expensive access systems that are capable of lifting components to the nacelle such as jack ups or lift boats. In order to do so, preventive maintenance might be useful. 3.2 Preventive maintenance As corrective maintenance alone is very costly, preventive maintenance becomes more and more important in the maintenance strategy. Preventive maintenance as maintenance, which is carried out at predetermined intervals or according to prescribed criteria and intended to reduce the probability of failure or the degradation or functioning of an item [60]. Preventive maintenance can be subdivided into scheduled maintenance and condition based maintenance, as Figure 3-1 showed at the beginning of this chapter Scheduled maintenance Scheduled maintenance is maintaining wind turbine components in accordance with an established time-schedule or established production. Scheduled maintenance can be subdivided into clock-based maintenance or age-based maintenance. Clock-based maintenance is carried out at specified calendar times, where age-based maintenance requires that the maintenance is carried out when a component reaches a certain age. The age of a component does not have to be measured in calendar time, but often it is measured in revolutions or operational time [50]. By this type of maintenance, replacement of components can be planned early in advance and long downtimes due to failures can be prevented. However the full component lifetime will not be reached and therewith replacements could be unnecessary as are the high costs of gearbox components. In order to use gearbox components for their full lifetime, without risking high corrective maintenance costs CBM might be a more promising maintenance strategy Condition based maintenance CBM is performed after one or more indicators show that a component is going to fail or that its performance is deteriorating. The condition of wind turbine components and in this thesis especially the gearbox can be analysed both visually, by scheduled inspection visits, or by remote monitoring by the help of CMSs. What they comprehend and estimates of their costs are given in this section Scheduled inspection visits As a way to estimate the condition of the gearbox and its components, service crews visit the wind turbines offshore at a regular interval. Normally once or twice a year service teams inspect the wind turbine components. If the crew sees an anomaly or a component, which is at the end of its lifetime, a repair operation is scheduled. The equations in Appendix B can be applied to estimate the costs of scheduled inspection visits. These are only a fraction of the costs of a gearbox replacement.

50 26 There are no or relatively low component costs for a scheduled inspection visit. Only the costs of some consumables need to be taken into account. For instance the oil that needs to be replaced once every two years. Labour costs are small as the scheduled inspection visits take only a couple of hours, depending on the wind farm distance. This also has its effects on low or negligible production losses. Proper scheduling can completely prevent production losses as days of low or no wind should be chosen for the scheduled maintenance action. Finally the access costs are significantly lower as only the relatively cheap CTV is needed. An operator can also choose to make use of a helicopter to transport the maintenance crew, though this might be more expensive in most cases. Labour and access costs for a scheduled maintenance action are relatively easy to estimate and will not differ a lot per regular inspection visit. Each scheduled inspection visit to the wind farm comes down to a rough estimate of to for a 6 MW offshore wind turbine [61]. Herein the access costs are the dominant cost driver. This is comparable to the scheduled maintenance costs of Kentish Flats [5], a wind farm located off the coast of Kent, England. At the end of this chapter a cost-benefit analysis shows that reducing the number of scheduled inspection visits can be a significant cost saving, if this does not lead to reduced wind turbine availability. In order to make sure this does not happen, a CMS could be a solution Condition monitoring systems A way of analysing the condition of components at a remote location is by the help of a CMS. This means that there has to be some sort of wear or damage detected to the component by the system, which can cause a more severe failure. If this is the case the component is replaced or repaired, thus preventing secondary damage and high downtime. If applied correctly this maintenance strategy is able to decrease maintenance costs by an increase in efficiency of wind turbine visits and component use. As repair actions are only carried out when necessary, unlike time based maintenance the components optimal lifetime is used. In this section the principles of gearbox CMSs are explained briefly. Though there are a lot of different developments and techniques in CMSs, in general they all provide similar benefits [62] [63]: 1. Avoidance of premature breakdown: The main feature of CMSs is to detect incipient failures and to prevent catastrophic failures and secondary defects. For example, late detection of a bearing fault may lead to the replacement of the entire gearbox. 2. Reduction of maintenance costs: With on-line monitoring the number of visual inspections by certified crew can be decreased. Also the replacement of intact parts might be avoided by CMSs. 3. Supervision at remote sites, remote diagnosis: As wind farms are tend to be built at more remote places, optical and acoustical changes will not get noticed for quite a long time, in contrast to industrial machinery. CMSs can detect those changes at an early time and send a warning and diagnostic details to the maintenance staff. 4. Improvement of the capacity factor: With early warning of impending failures, repair actions need not to be taken immediately. They can be carried out in a period of low

51 27 wind speeds when the wind turbine is off-line, so without affecting the capacity factor. 5. Support for further development of wind turbines: As an extra benefit, CMSs provide detailed information of the dynamic behaviour of a wind turbine over long periods of time that may help optimizing the wind turbine design. The main components a Condition Monitoring System consists of are: measurement sensors, a communication network, a data-mining technique. Sensors installed in the wind turbine measure various parameters, which are affected by the component health status. The data from the sensors is subsequently sent to a data acquisition station by a communication network between the turbine and the operator. Finally data-mining technique analyses the data and gives an indication of the condition of the wind turbine component. If the system detects an anomaly or deteriorating health status an alarm is set on and maintenance can be scheduled. A list of commercially available CMSs, used for wind turbine gearbox monitoring, can be found in Appendix E. At first the measurement techniques are described, consisting of sensors mounted on the gearbox. These sensors acquire data about the health status of the gearbox components. The techniques mentioned in this work are respectively vibration measurements, acoustic emission and oil-contamination monitoring, as they are most frequently mentioned for gearbox condition monitoring Measurement techniques In a wind turbine sensors already collect data coming from standard measurements of temperature, wind speed, power output etc. (SCADA), which gives the operator general information of the operational status of the wind turbine. More specialised measurement techniques should provide the operator better insight in the health status of the gearbox. In the next section three specific techniques are detailed, most commonly used for gearbox condition monitoring. Vibration Measurement Vibration analysis is the most known technology applied for condition monitoring, especially for rotating components such as the gearbox [63]. For vibration measurements different sensors are currently used, depending on the component and the monitored loads (the loads of interest), i.e. accelerometers, velocity- or displacement sensors. For the main bearing which operates at a low speed displacement sensors are typically of interest [64]. All rotating equipment vibrates to some degree, but as bearings and components reach the end of their product life, they begin to vibrate more dramatically and in distinct ways. Sensors placed on the bearing housing or gear case are used to detect characteristic vibration signatures. This signature is unique for each gear mesh or rolling-element and depends on the geometry, load and speed of the components. The data-mining technique then compares the signature during operation with the characteristic signature and flags any anomalies. Ongoing monitoring of equipment allows these signs of wear and damage to be identified well before the damage becomes an expensive problem [64].

52 28 This technique is already extensively used in all kinds of industries for many years. However in case this technique is applied on wind turbines one has to bear in mind that there are differences with respect to other industries, like the dynamic load characteristics and low rotational speeds. In other applications, loads and speed are often constant during longer periods, which simplify the signal analysis. Wind turbines are much more dynamic and therefore harder to analyse [65]. Acoustic emission For acoustic emission (AE) higher frequencies are considered than for vibration analysis. It uses high-frequency, narrow band vibration sensors to detect structure-borne pulses that occur when a rolling contact or gear mesh encounters a discontinuity in the surface, that indicates wear or debris particles. These higher frequencies give an indication of the starting effects and will normally weaken out after a short period of time. AE measurement features very high frequencies compared to other methods, so the costs of data acquisition systems with high sampling rates need to be considered [66]. Oil-contamination monitoring measurements An oil-contamination sensor counts the alien particles in the lubrication or hydraulic oil. The number of particles gives an indication of the wear of the component. Several forms of contamination monitoring techniques are available. One technique applies an electromagnetic field to a contained fluid to detect the presence of ferro-magnetic debris, which is indicative of wear of a certain component. Normally the fluid is the oil used for lubrications of the bearings or the oil used in the turbine hydraulics. Another type of condition monitoring uses a laser light source to count the particles, seen as obstructions, in a fluid stream. A third system passes fluid over a fine mesh screen and detects the pressure drop as an indicator of accumulated contamination [67] Communication network Modern wind turbines already communicate with the controller station, mainly for operational output such as rotational speed, wind speed and electrical output. Wireless transfer of data packs containing 10 minute averages are send and gives the controller information how well and in what state the turbine is operating in, by SCADA data. Ethernet networks with TCP/IP (Transmission Control Protocol/Internet Protocol) or WLAN (Wireless Local Area Networks) are used for communication within a wind turbine itself and from the wind turbine to the outside world [50]. The Standard IEC (Information-Education-Communication) should be used as a basis for the data exchange and communication software and procedures between one or more CMSs, the wind turbine controller, the SCADA-System and human users. This Standard can lower the costs of integration and maintenance for all parties involved. Wind turbine vendors have the opportunity to focus their core business: creation of facilities for efficient and optimized operation of wind turbines by including a standard communication solution in their product portfolio [68] Data-mining techniques To interpret the data given by the sensors on the gearbox in such a way that a failure is detected at a very early stage, a data-mining technique is necessary. Research reveals that a lot of different techniques can be used to analyse the sensor data. Examples of such

53 29 techniques are Spectral Analysis, Neural Networks and Support Vector Machines. As these are often still in an experimental phase with respect to gearbox monitoring, the true performance of each technique remains uncertain. However different studies reveal their possibilities with respect to wind turbine data-mining and detecting a failure at an early stage, especially for the gearbox [16], [69] - [75]. A good combination of the three CMS components enables wind turbine operators to accurately detect the deterioration of a gearbox component and more importantly to detect it in time. Certainly offshore a maintenance or repair visit takes a lot of time and planning CMS costs The costs of a CMS installed in a wind turbine consist of both the initial investment costs at the beginning of the turbines operating lifetime and the CMS operational costs. Nilsson and Bertling estimated the investment costs of a CMS to be [5]. Yang et al. found a gearbox CMS of [76]. These cost estimation fit with the research by van Horenbeek et al., which gave a price indication of for a CMS system consisting of oil and vibration analysis [13]. According to Grahm & Juhl the costs of a sophisticated CMS for the main wind turbine components consist of [77]: 1. Around hardware costs per wind turbine (in case of multiple turbines a project discount is applied) 2. Around installation costs per wind turbine for establishing the data structure, database and alarm settings for the first wind turbine. 750 for the following wind turbines. 4. A remote diagnostic contract for 1000 per wind turbine per year, herewith outsourcing the condition monitoring of the wind turbine. This however is optional and might be done by the wind farm operator itself as well. In order to profitably install such a system in a wind turbine the revenue should outbalance these costs. The revenues made by a CMS can be set in several ways. At first expensive repairs can be prevented by early detection of component deterioration and preventing secondary damage and high downtime. Secondly CMSs might be used to increase the capacity factor of the wind turbine. This can be achieved by reducing the downtime of the wind turbine or by better scheduling of maintenance and repair actions to avoid downtime due to weather restrictions or to avoid high production losses by scheduling repair actions during low wind conditions. Finally the implementation of a CMS can reduce the necessary visit rate of the wind turbine, because unnecessary preventive inspections can be excluded based on the condition readings of the CMS. Nowadays it is common to visit each turbine one or two times a year [5] CMS capacity Some literature about the performance of CMSs is briefly described in this section. A wide variety of papers describe the abilities of CMS to detect gearbox failures, but unfortunately these are in most cases not very specific. However van Horenbeek et al. [28] found detailed information per failure mode for a CMS consisting of both a vibration analysis system and an oil debris monitor. The results are shown in Table 3-2, where the detectability of the first six most costly failure modes is given. It shows that not every failure can be detected by this combination of CMSs. However the other failures are found at high detectability levels, and

54 30 very important as well at an early stage of deterioration. This means that large or catastrophic failures might be prevented to some extent. Failure mode Table 3-2 CMS capacity as found by van Horenbeek et al. [28] 3 Share of total failure costs Detectability by CMS Deterioration status 1. High speed shaft 27,8% 100% 5% bearing failure 2. Broken intermediate 21,2% 70% 5% shaft 3. Intermediate shaft 10,1% 0 - bearing failure 4. Planet bearing 9,6% 100% 20% failure 5. Broken center post 6,2% 0-6. High speed shaft bearing black spot 5,4% 100% 20% To second this data about the capabilities of CMSs, a case story from Jon Pedersen, Site Manager for Rødsand II Wind Farm is cited, where he states the cost-effectiveness of a CMS from Gram & Juhl (TCM) with respect to catastrophic gearbox failure prevention [56]. This example indicates the usefulness of CMSs and its capabilities to prevent large gearbox failures: In August 2012, we received an alarm: High Speed Pinion on Turbine L01. Thereafter, we visually checked to see if we could see some cracks on the gear teeth. Additionally, we checked the in-line oil filter to make sure there were no shavings from the metal. When we didn t see any signs of cracks or shavings, we decided that we would start up the turbine again. But after two days, we received the same alarm again. We repeated the procedure, but we still didn t find any cracks or metal shavings, so we started the wind turbine again, and immediately ordered a new high speed shaft. The reasoning behind the purchase of the new shaft was a simple calculation... if there was a tooth that might break off and ruin a gearbox, the replacement cost for a new gearbox would be around $ including a jack-up rig, whereas the replacement of a new shaft would only cost $ After changing the shaft we did not become much wiser... was there a crack? We cleaned our old shaft using Flawfinder (cleaning spray from Rocol). After that, we could conclude, that it was a really good decision to replace the shaft. We were finally able to see the crack. A study by Crabtree et al. [16] shows how a vibration analysis system and an oil debris counter were able to detect an incipient failure and give a significant warning, up to two months, of damage, which could lead to a gearbox failure. This means that even though a large gearbox repair cannot be prevented, two months of downtime as a result of weather inaccessibility or lack of stock parts or vessel mobilisation might be prevented. Finally Grahm & Juhl, a leading supplier of CMS for wind turbines, claims that with their CMS potentially 1 day of downtime per year, caused by the gearbox, is prevented. 3 CMS consists of a vibration measurement and oil analysis

55 31 To find out whether or not a reduction in inspection visits can pay back for the investment and operational costs of a CMS on its own, CMS costs are compared with the costs of inspection visits for a single wind turbine near shore. Assumptions made in this analysis are: One CTV is rented for a day Maintenance crew can check 4 wind turbines in one day A maintenance crew consists of 4 engineers, including boat crew No downtime is considered, as the inspection visit is scheduled at very low wind speeds only The cost of one inspection visit comes then down to approximately per turbine, estimated by Equation B-1 - Equation B-4, thus only including vessel and labour costs. The costs made by scheduled inspection visits over lifetime are given by Table 3-3 for different intervals. Table 3-3 Lifetime maintenance costs for different scheduled inspection visits a year Number of scheduled inspection visits Lifetime maintenance costs per year , ,00 ½ ,00 ¼ ,00 Figure 3-5 shows that the implementation of a CMS is profitable if it will lower the necessary scheduled inspection visits from two times a year to one or less visits a year, assuming CMS cost of` and However for a CMS, with lifetime cost of , further reduction of inspection visits is required to become profitable by this feature on its own. For a wind farm with yearly inspection visits, the implementation of a CMS can only return the investment and operational costs of the CMS by reducing the number of scheduled inspection visit by a factor 4, assuming lifetime CMS cost of (Figure 3-6). This means that instead of a yearly inspection, the gearbox is visually inspected once every 4 year. For a CMS system with lifetime cost of or , Figure 3-6 shows that the CMS cannot become profitable by solely reducing the lifetime inspection visits. In such case, failures or downtime by failures need to be prevented additionally to become profitable.

56 CMS profit in turbines lifetime CMS profit in turbines lifetime , , ,00 - (20.000,00) (40.000,00) (60.000,00) (80.000,00) 0,25 0,5 1 2 Number of visits per year with CMS CMS costs CMS costs CMS costs Figure 3-5 Profit CMS by reducing the number of wind turbine visits from two visits a year , ,00 - (10.000,00) (20.000,00) (30.000,00) (40.000,00) (50.000,00) (60.000,00) 0,25 0,5 1 Number of visits per year with CMS CMS costs CMS costs CMS costs Figure 3-6 Profit CMS by reducing the number of visits per year from one visit a year Though this section gives a brief indication of what CMSs are capable of with respect to wind turbine gearboxes, this research focuses on the capacity a CMS should have. A sensitivity analysis in Chapter 5 shows what a CMS should be capable of, to outbalance the CMS costs and to become profitable in the wind turbine lifetime by preventing large failures and downtime.

57 33 Chapter 4. Cost model Before presenting the results, the cost model used for the sensitivity and cost-benefit analyses is explained. The method used and the integration of several input parameters are described in this chapter. 4.1 Monte Carlo method The MC method is able to deal with complex systems, while accounting for uncertainty and risk in a quantitative analysis. It is a computerized mathematical technique and is used in a wide variety from economics to nuclear physics. With MC methods, a large system can be sampled in a number of random configurations, and that data can be used to describe the system as a whole. For more detailed information about the theory of the MC method, the researcher likes to refer to [78] and [79]. The MC method is integrated in the cost model, used in this thesis to estimate the lifetime costs of gearbox maintenance. Variables which vary per iteration, because they use probability distributions or contain a high degree of uncertainty, are: The occurrence of a failure, per failure category, in the wind turbine lifetime. Modelled by a discrete distribution in which the likelihood of occurrence of each gearbox maintenance category is defined. This is explained in more detail in 4.2 and 4.3. Downtime per failure. This is uniformly distributed in the model as all values have an equal chance of occurring, within a specified minimum and maximum. More detailed explanation can be found in The component costs in case of a large or a small failure and the component cost savings for a gearbox replacement, which can be refurbished. This is described in more detail in 4.2 and 4.3 (also uniformly distributed). A schematic representation of the cost model, with the input parameters, output parameters and the iteration procedure of the MC method, is given in Figure 4-1. The parameters mentioned in the figure are subsequently explained in more detail in the remainder of this chapter. The m-files, describing the model can be found in Appendix C. The blue blocks represent the overall cost model, with the lifetime gearbox maintenance costs as output and inputs depending on the case study that is analysed. The red part in Figure 4-1 represents the MC simulation per case study in which there are fixed parameters, which do not differ per iteration and the iteration variables, which are described above. It shows that both the fixed parameters and the iteration variables are combined in a simulation and the lifetime gearbox maintenance costs of a wind turbine are estimated. To obtain a probability distribution of the costs, this simulation is repeated N times, while the iteration variables vary by their random configurations.

58 34 Case study variables Distance to shore Gearbox failure rate CMS (with or without) CMS costs Lifetime gearbox maintenance costs Figure 4-1 Schematic representation of the cost model, in which the red part represents the MC simulation

59 Gearbox maintenance categories Four categories of gearbox maintenance are implemented, these are: F 1 : a catastrophic gearbox failure (replacement of the gearbox), F 2 : a large gearbox failure (the replacement of a larger component), F 3 : a small gearbox failure (small repair action), S: visual inspection and service visit. Corrective maintenance costs over the wind turbine lifetime are estimated by: Equation 4-1 Cost estimation of the lifetime gearbox maintenance for one wind turbine offshore Where C F1, C F1ref, C F2, C F3 and C S represent the costs of each maintenance action and a, b, c, d and e the number of times each action has to be performed in the wind turbine lifetime. One of the most difficult parameters to find or estimate is the partition of the gearbox failures into the different categories, as this differs per wind turbine type, location and maintenance strategy. To account for uncertainties different failure rate partitions are analysed for each case study. The low and high estimated partitions of gearbox failures are based on respectively the DOWEC baseline (Weak) [80] and WMEP data by Faulstich et al. (Robust) [81]. Weak means that the share of large and catastrophic gearbox failures, in the total number of failures (failure rate), is high. Robust represents relatively higher numbers of small gearbox failures and less large and catastrophic gearbox failures. The average of both partitions is called medium Catastrophic gearbox failure In the worst case scenario the entire gearbox needs to be replaced (F 1 ), due to major damage. In order to replace the gearbox a jack up barge is necessary. The costs involved are explained in in The probability of a catastrophic gearbox failure, which requires the replacement of the gearbox, P gr, is given by: Equation 4-2 Probability of a catastrophic gearbox failure per year in which FR represents the gearbox annual failure rate and P 1,cat the probability that the gearbox failure is a catastrophic gearbox failure for the different failure rate partition categories, weak, medium and robust. If a gearbox cannot be refurbished the gearbox is considered to be a total loss. Based on infield experience it is assumed that the majority of the gearboxes can be refurbished [82]. Component cost savings in the simulations then can sum up to to The probability of a catastrophic gearbox failure, which requires the replacement of the gearbox, but which can be refurbished, P gr,ref, is given by: Equation 4-3 Probability of a catastrophic gearbox failure per year, which can be refurbished for a next event

60 36 represents the chance that a failed gearbox can be considered reusable and is assumed to be 75%. As an extra assumption given in the MC simulations, it is assumed that at least one gearbox is replaced during lifetime [83]. Examples of failure modes, which require the replacement of the gearbox as they cannot be repaired in situ, are planet bearing failures, intermediate shaft bearing failures and a broken centre post [13] Large gearbox failure In case of a large gearbox failure (F 2 ), a large component of the gearbox has to be replaced. To lift the component from and to the landing platform a lift boat is needed. The internal crane is in this case able to hoist the component out of and into the nacelle. A large gearbox failure still represents significant costs in component and transport. In the simulations component costs vary randomly from to The transport costs are high due to a need of a lift boat, of which the day rate can be found in The yearly probability of a large gearbox failure,, is given by the following equation: Equation 4-4 Probability of a large gearbox failure per year where represents the probability that the gearbox failure is a large gearbox failure for the different failure rate partition categories, weak, medium and robust, as given in Table Small gearbox failure Small gearbox failures (F3) represent the failures, which need repair or adjustments with no or only small components that can be transferred by a CTV and carried up to the nacelle. This causes only little downtime and relatively low costs. In the simulations component costs vary randomly from to for a 6MW wind turbine. The yearly probability of a small gearbox failure ( is given by the following equation: Equation 4-5 Probability of a small gearbox failure per year is the probability that the gearbox failure is a small gearbox failure for the different failure rate partition categories, weak, medium and robust Service and inspection visit A service and inspection visit (S) is simulated once a year to inspect, clean and service the entire wind turbine. Costs and downtime are minor; as this action can be scheduled in advance and therewith prevent times of high winds and downtime. A summary of the costs and parameters, as used for the simulations presented in Chapter 5, can be found in Table 4-1.

61 37 Table 4-1 Estimated parameters for each failure category for a 6 MW wind turbine 30 km offshore Category Component costs Percentage of gearbox failures Marine system [44] [ ] [%] Weak [80] Medium Robust [81] F Jack up barge + 2 CTV F Lift boat + CTV F CTV S CTV 4.3 Integration of failure probability As an example how probability of failure is integrated in the model, one iteration to determine the number of catastrophic gearbox failures (F 1 ) in a wind turbine lifetime is described in this section. In this simulation a failure rate of 0.25 and the weak failure rate partition is used. The probability of a catastrophic gearbox failure in that case is given by: And probability of a gearbox replacement, which can be refurbished by: To determine the number of catastrophic gearbox failures, the probability of each failure is sampled with random values between zero and one for each year of operation, as is described in Table In case the gearbox can be refurbished for a next event, component cost savings are simulated, varying uniformly from to per failure.

62 38 Table 4-2 MC model working principle with respect to number of F 1 failures over the wind turbine lifetime Year of operation Random value <? : Is a gearbox replacement required? <?: Can the gearbox be refurbished? 1 0,8913 <0,0425? No - 2 0,0459 <0,0425? No - 3 0,8691 <0,0425? No - 4 0,0323 <0,0425? Yes < ? No 5 0,3702 <0,0425? No - 6 0,2267 <0,0425? No - 7 0,4081 <0,0425? No - 8 0,3809 <0,0425? No - 9 0,6501 <0,0425? No ,0291 <0,0425? Yes < ? Yes 11 0,6899 <0,0425? No ,8691 <0,0425? No ,4602 <0,0425? No ,9301 <0,0425? No ,1613 <0,0425? No ,4233 <0,0425? No ,9888 <0,0425? No ,7557 <0,0425? No ,2268 <0,0425? No ,4240 <0,0425? No - The exemplary iteration result indicates that two gearboxes were replaced during 20 years. One of them was considered a total loss, whereas the other one could be refurbished, resulting in less dramatic component costs. Similar simulations are performed for F 2 and F 3, resulting in the number of large and small repair actions in one wind turbines lifetime. The number of inspection visits is not dependent on probability statistics but is set at a fixed 20 times, representing yearly inspection visits. The cost savings of the gearbox refurbishment are simulated by again using the random function of Matlab, within set lower and upper boundaries, as Equation 4-6 shows: in which Equation 4-6 Simulation of component costs ( ) represents the component cost savings in case of a gearbox refurbishment, and the lower and upper boundary conditions, and in which rand gives any random value between zero and one. In this case the cost savings are uniformly distributed between the lower and upper boundaries given in 4.2 and the outcome is extracted from the costs of a gearbox replacement with total loss. Equation 4-7 also holds and is used for the simulation of the component costs of a large and small gearbox failure, between their own cost boundaries.

63 39 Multiplying the number of occurrences of each failure category with the costs and adding them together as in Equation 4-1, the total gearbox maintenance costs of one wind turbine are simulated. In order to get the Weibull distributions and a proper mean and standard deviation, these simulations are performed times for each analysis. This represents the outcome in gearbox maintenance costs over lifetime of wind turbines. The mean gearbox maintenance costs are given by: Equation 4-8 Mean gearbox maintenance costs of simulations And standard deviation by: Equation 4-9 Standard deviation of the lifetime gearbox maintenance costs (( ) ) The Weibull distribution is given by a two-parameter Weibull function as given by Equation 4-10: Equation 4-11 Two parameter Weibull distribution function ( ) In which y represents the probability of occurrence, x the lifetime maintenance costs of a gearbox in this thesis, a the scale factor and b the shape factor as calculated by Matlab. A table containing the mean lifetime maintenance costs, standard deviations and scale and shape factors used for the Weibull distribution as presented in Chapter 5, are listed in Appendix D. 4.4 Integration of CMS In order to decrease the maintenance costs a CMS might be installed. In the second part of each case study, presented in Chapter 5, a CMS is implemented in the simulation to analyse the potential revenue of a CMS and to estimate the required performance to become profitable. The CMS is implemented in the MC simulation to perform the following tasks: 1) Reduce the number of large and catastrophic gearbox failures, which leads to less component-, vessel and labour costs as well as reduced production losses. In the cost model this is implemented by failure detection efficiency. 2) Give an early incipient failure alarm for cost-effective maintenance scheduling to reduce production losses, with representing the prediction time before manual shut down or functional failure.

64 40 The implementation and potential effects of the CMS performance on the gearbox maintenance actions are schematically presented by Figure 4-2, after which both and are explained in more detail. CMS detects large or catastrophic incipient failure > F 1 and F 2 can be prevented by a small repair action in time < > F 1 and F 2 cannot be prevented by a small repair action < Minor costs: Small gearbox repair, with no additional waiting downtime Low costs: Small gearbox repair, with additional waiting downtime High costs: Large repair, but with no waiting downtime Extremely high costs: Large repair, with additional waiting downtime Figure 4-2 Schematic representation of the implementation of CMS performance parameters and Reduce number of large and catastrophic failures The CMS performance parameter, is quite straightforward to implement in the MC simulations. The CMS failure detection efficiency,, in this thesis is defined as: The reduced probability of catastrophic (F1) and large (F2) gearbox failures, which are prevented by an early CMS alarm and the resulting preventive maintenance action (F3). This means that for a CMS failure detection efficiency of 25% the probability of both catastrophic and large failures are reduced with a factor 0,75 5, as indicated by Equation 4-12 for the catastrophic gearbox failures: Equation 4-12 Implementation of for the probability of a gearbox replacement with a total loss The reduction in probability of large and catastrophic gearbox failures is subsequently added to the probability of a small gearbox failure, like: Equation 4-13 Implementation of for the probability of a small gearbox failure To increase confidence and accounting for uncertainties, the required failure detection efficiency,, are round off upwards to whole percentages. 5 =1-0,25

65 Give an early warning to reduce waiting downtime As a second manner to decrease the maintenance costs, cost-effective maintenance scheduling by CMSs is implemented in the MC simulations to minimise waiting downtime. In order to implement the second performance parameter, an explanation is given what is meant with waiting downtime and cost-effective scheduling. Waiting downtime is the downtime caused by weather window waiting time, the mobilisation of transport and lifting vessels and the order and logistic time of spare components. This thus excludes the downtime caused by the actual repair at the turbine. In case the CMS gives an alarm for an incipient large or catastrophic failure (top left branch in Figure 4-2), there is limited time in which the operator can act to prevent the failure and the downtime by a small repair action. This is visualized by the top and middle plot of Figure 4-3. As well the moment of the CMS alarm, T 1, is important as well. should be sufficiently long for scheduling the repair action. At T 2 the operator is forced to shut down the wind turbine, as further deterioration of the failed gearbox component will cancel out prevention of the large or catastrophic failure by a small repair action. As the operator shuts down the turbine, downtime results in production losses, which is indicated in the situation depicted by the middle plot of Figure 4-3. If the time before fatal deterioration of the gearbox component, is higher than the time needed,, the large or catastrophic failure alarm is considered sufficiently in time and waiting downtime is prevented: Equation 4-14 Condition a CMS has to fulfill to prevent waiting downtime for a small repair action In case a CMS is unable to prevent a large or catastrophic failure and repair action (bottom left branch in Figure 4-2), still significant cost savings can be made when deterioration is detected prior to the functional failure. in the bottom plot of Figure 4-3 shows the time there is to schedule and execute a repair action without waiting downtime. As in this case a small repair action will not suffice anymore, the repair preparation time before repair takes longer. With respect to a small repair action, a large or catastrophic repair action could require significantly longer component ordering time. Secondly the mobilisation of a large lifting vessel or jack up barge can take up to a few weeks to even months, as described in Table 4-3.

66 42 Waiting downtime Figure 4-3 Cost-effective scheduling by CMS in case of: Top: A prevented large or catastrophic gearbox failure by a small repair action. Waiting downtime is prevented, because > Middle: A prevented large or catastrophic gearbox failure by a small repair action. Waiting downtime is not entirely prevented, because < Bottom: A large or catastrophic gearbox failure. Waiting downtime is prevented, because >

67 43 If the prediction time before the functional failure, is higher than the time needed before the actual repair at the wind turbine starts,, the large or catastrophic failure alarm is considered in time with respect to waiting downtime: Equation 4-15 Condition a CMS has to fulfill to prevent waiting downtime for a large or catastrophic repair As Equation 4-14 and Equation 4-15 indicate, depends on. is the time needed to schedule and execute a repair action. This involves time to mobilize a vessel and crew, logistics of spare parts, the actual repair time and the weather window waiting time. The estimations of each and subsequently the best and worst case of are given in Table 4-3. Table 4-3 Estimated parameters downtime in days Small repair action Large repair action Gearbox replacement Time to mobilize vessel [84] Spare part logistic time [40] Repair time [84] WW 6 waiting time bestworst case 7 [85] best case worst case To have a CMS with optimal scheduling time performance, needs to be greater then in all cases, thus greater than the worst case scenario, given in the final column of Table 4-3. Here only the repair time has to be considered as downtime. In such case a CMS is able to reduce the downtime of a gearbox replacement from 147 days in the worst case scenario to 3 days in the best case, saving approximately half a million euro for a 6 MW wind turbine. As component ordering time, vessel mobilisation, and weather window waiting time can differ significantly per failure, they are simulated to vary in a uniform distribution between the best and the worst case scenario in the model. In the MC simulation, is implemented in the downtime costs calculation per failure: Equation 16 Implementation of in the downtime costs calculation per failure ( ) 6 WW stands for weather window, which is the time the wave and wind conditions are below the limits of the vessels used per repair action. 7 Maximum days of waiting time for a CTV with a maximum wave height of 2.5m is 63 days, according to O'Connor et al.. However in case such a waiting time is foreseen a helicopter could be used to prevent the long waiting time, as the helicopter is only limited by wind speed.

68 44 of which: Equation 17 Waiting downtime where represents the total downtime costs for failure i, which can be F 1, F 2 and F 3. represent the costs of one day of downtime (calculated as in Appendix B), is the downtime for the actual repair at the wind turbine for failure i, and weather window waiting time. the downtime due to mobilisation of a vessel, spare part logistics and The simulations are performed with high iteration numbers (N= If N is decreased the variability becomes more apparent as is clearly visible in Figure 4-4, which presents the CMS profit by. This means that the interpretation of required CMS performance should be defined more clearly. Thus the CMS breaks even where on average the CMS costs are equal to the CMS revenue. This roughly means that in 50% of the time the CMS has a profit at that performance and in 50% there still is a negative balance after 20 years of operation. Figure 4-4 Variability of MC results as N is decreased from a single simulation for N= (middle red line) to 10 simulations with N= (blue lines)

69 45 Chapter 5. Sensitivity analysis and CMS profitability In this chapter the results of the cost model are discussed. The model estimates the lifetime costs of gearbox maintenance for a 6 MW offshore wind turbine. It gives an approximation of the costs and the sensitivity to different parameters. Besides a simulation of an offshore wind farm close to shore, a sensitivity analysis is performed for wind farm distance to shore and gearbox failure rate. At the end of each case study the revenue of an added CMS is estimated. 5.1 Base case: near shore 6MW WT The first case represents a near shore 6 MW wind turbine, with an annual gearbox failure rate of This case is used as a base to compare wind farm variables, like gearbox wind farm distance and failure rate. The implementation of distance to shore and failure rate in the MC simulations are explained in the following sections Without CMS At first the results of a gearbox without a CMS are presented. In Figure 5-1 the results are displayed in a probability distribution of the total gearbox maintenance costs, revealing significant differences between the different failure rate partitions as given in Table 5-1. Scale and shape parameters of the Weibull distribution can be found in Appendix D.1. By running the cost model, the basic lifetime maintenance costs, for a near shore wind turbine gearbox with an annual failure rate of 0.15, are estimated. The results can be found in Table 5-1. Table 5-1 Results of Monte Carlo Simulation for the standard situation Mean [x10 6 ] Standard deviation [x10 6 ] Weak Medium Robust Weak Medium Robust 2,33 (+13%) 2,07 1,82 (-12%) 1,00 (+14%) 0, (-16%) Here the large effect the gearbox failure rate partition has on the lifetime gearbox maintenance costs is visualized. The weak gearbox will results in 28% higher gearbox maintenance costs over the wind turbine lifetime, compared to the robust gearbox. Secondly the standard deviations are very high, which indicate the high uncertainty of lifetime gearbox costs.

70 46 Figure 5-1 Weibull distributions of the lifetime maintenance costs of a 6 MW wind turbine gearbox near shore for three different failure rate partitions With CMS In order to find out what a CMS might mean for the gearbox maintenance costs and what it has to be capable of to become profitable, a CMS is implemented in the simulations. At first the effects of and are analysed separately before their combined benefit is presented CMS implemented to reduce large and catastrophic gearbox failures Table 5-1 shows that the total lifetime gearbox costs can run up to two million euro per turbine. The main shares in the gearbox maintenance costs are a result of large and catastrophic gearbox failures. Therefore a reduction in failure rate of such failures could lead to significant cost savings. In this section we investigate the effect of on the maintenance costs as well as the required performance of a CMS to break even. The cost model gives an indication of the costs and the reduction in costs for different, shown in Table 5-2 and in Figure 5-2. The percentages in Table 5-2 represent the difference compared to a CMS with. Scale and shape parameters of the Weibull distribution can be found in Appendix D.3.

71 47 Table 5-2 MC results for gearbox maintenance costs with a CMS system for different values of [%] Mean costs [x10 6 ] Standard deviation [x10 6 ] Weak Medium Robust Weak Medium Robust 0 2,33 2,07 1,82 1,00 0,88 0, ,05 (-12%) 1,86 (-10%) 1,67 (-8%) 0,87 (-13%) 0,77 (-13%) 0,65 (-12%) 50 1,78 (-24%) 1,66 (-20%) 1,52 (-16%) 0,72 (-28%) 0,64 (-27%) 0,55 (-26%) 75 1,51 1,45 1,38 0,53 0,48 0,41 (-35%) 100 1,24 (-47%) (-30%) 1,24 (-40%) (-24%) 1,24 (-32%) (-47%) 0,21 (-79%) (-45%) 0,21 (-76%) (-45%) 0,21 (-72%) Figure 5-2 Weibull distribution of gearbox maintenance costs as a function of rate partition for the medium failure The results show that with a CMS significant cost savings can be achieved. Table 5-2 shows that an increase of with 25% results in 8% to 12% gearbox maintenance cost reduction. In addition allows the CMS for better cost indication as indicated by the lower standard deviation. The profit by different values of is given in Table 5-3, assuming CMS lifetime costs of It shows that the robust failure rate partition has the lowest margins, with respect to cost reductions by. Very high CMS revenues are seen for the weak failure rate partition.

72 48 Table 5-3 Mean profit for different values of [%] Mean profit CMS by [x10 5 ] Weak Medium Robust 25 2,57 1,83 1, ,24 3,87 2, ,92 5,97 4, ,64 8,08 5,52 Table 5-3 shows that for the given, it is clearly profitable to install a CMS capable of reaching these values of. To show what value of is minimally needed for profitable installation of a CMS system, consisting of vibration and oil debris monitors, Figure 5-3 is plotted. Figure 5-3 reveals that a CMS system with total costs of should minimally have a detection efficiency of 3% in case of the weak gearbox and 5% in case of the robust system. Figure 5-3 CMS profit as a function of Figure 5-3 visualizes the desired CMS detection efficiency for a fixed CMS price of , as estimated in More sophisticated systems are most likely more expensive as well. This has to result in higher CMS performances to make the system profitable. To see what effect the CMS price has on the required for profitable implementation, simulations are performed in which the price of the CMS is varied. The results for the robust failure rate partition are analyzed, as this is the scenario with the highest performance demand of the CMS. Figure 5-4 reveals that lifetime CMS costs of demand a detection efficiency of at least 7% to become profitable and lifetime CMS costs of demands at least a CMS detection efficiency of 10%. The CMS price does not necessarily springs from a higher CMS investment price. This could also reflect on higher numbers of false alarms during the wind turbine lifetime. The stricter

73 49 the CMS alarm levels, the higher the probability of detecting a failure and detecting it in time. This however also means that more false alarms can be expected. The costs made by these false alarms are considered as a part of the CMS lifetime costs in the case studies and are a result of the costs of a visual inspection visit of a maintenance crew, which is custom after a CMS alarm. Figure 5-4 CMS profit for different CMS costs as a function of for the robust failure rate partition CMS implemented to reduce waiting downtime Similar to the previous section an analysis is made to find the effects of prediction time,, on the lifetime maintenance costs and to estimate the minimum prediction time, required to get to the Break-Even Point by. The MC simulations give an indication of the costs and the reduction in costs for different values of. The results are shown in Table 5-2 and in Figure 5-2 in case the CMS is able to predict respectively a week, a month and three months before the functional failure or manual shut down. The percentages indicate the difference with a CMS with. Scale and shape parameters of the Weibull distribution can be found in Appendix D.4.

74 50 Table 5-4 Gearbox maintenance costs without and with a CMS system for different values of [days] Mean costs [x10 6 ] Standard deviation [x10 6 ] Weak Medium Robust Weak Medium Robust 0 2,33 2,07 1,82 1,00 0,88 0,74 7 2,22 (-5%) 1,98 (-4%) 1,73 (-5%) 0,97 (-3%) 0,86 (-2%) 0,72 (-3%) 31 2,01 1,79 1,56 0,87 0,77 0,64 (-14%) 92 1,73 (-26%) (-14%) 1,55 (-25%) (-14%) 1,35 (-26%) (-13%) 0,72 (-28%) (-13%) 0,64 (-27%) (-14%) 0,54 (-27%) Figure 5-5 Monte Carlo simulations for gearbox maintenance costs and different values of medium failure rate partition for the The results show that if a CMS is able to prevent waiting downtime, significant cost savings can be achieved. The revenue of a CMS for different prediction times, is given in Table 5-3, assuming CMS lifetime costs of Table 5-5 Mean profit for different prediction times, [days] Mean profit CMS by [x10 5 ] Weak Medium Robust 7 0,70 0,67 0, ,83 2,55 2, ,72 5,01 4,38

75 51 Table 5-5 shows that a CMS with wind turbine. = 7 days is already profitable to install in an offshore To show what prediction time is minimally needed for profitable installation of a CMS, Figure 5-6 is plotted. Figure 5-6 reveals that a CMS system, with total costs of , should minimally be able to predict each failure 2 days in advance to become profitable. Figure 5-6 indicates a minimal difference between the different failure rate partitions, weak, medium and robust. However if the simulation is extended to three months as is visualized by Figure 5-7, a clear difference in profit after 20 years of operation becomes apparent. This can be explained by the fact that a short prediction time of only a couple of days is equally beneficial for the different failure categories, F 1, F 2 and F 3. In such case it does not matter, whether the failure is small, large or catastrophic. However long prediction time does have added value for large and, even more, for catastrophic failures. There is no extra benefit, in the simulation, for the small repair action if it is detected three months in advance compared to one month as the time needed for scheduling and executing the repair is significantly shorter. Figure 5-6 CMS profit as a function of to 10 days

76 52 Figure 5-7 CMS profit as a function of to 98 days Figure 5-6 and Figure 5-7 visualize the desired CMS detection time for a fixed CMS price of , as estimated in To see what the effect of the CMS price has on the required prediction time for profitable implementation, a Monte Carlo simulation is performed where the price of the CMS is varied. Again the robust failure rate partition is analyzed, as this is the scenario with the highest CMS performance demand for profitable installation. Figure 5-8 CMS profit for different CMS costs as a function of (Robust)

77 53 Figure 5-8 reveals that a lifetime CMS costs of demands a prediction time of 3 days to become profitable and lifetime CMS costs of demands at least 4 days of prediction time before functional failure or forced shut down Combined benefit of CMS functions In this section the combined effects of the CMS performance parameters on the lifetime gearbox maintenance costs are given.table 5-6, Table 5-7 and Table 5-8 give the gearbox lifetime costs for respectively the weak, medium and robust failure rate partition for different values of and. Subsequently in Figure 5-9 and in Figure 5-10 the reduction in lifetime costs are plotted, respectively as a function of and for the robust failure partition. The results show that large maintenance savings can be achieved as the performance of the CMS increases. These cost savings are the highest for the weak failure rate partition. Table 5-6 Lifetime maintenance costs for varying CMS performances for the weak failure rate partition 2,33 2,05 1,78 1,51 1,24 2,22 1,96 1,69 1,42 1,16 2,01 1,77 1,53 1,29 1,06 1,73 1,53 1,32 1,13 0,93 Table 5-7 Lifetime maintenance costs for varying CMS performances for the medium failure rate partition 2,07 1,86 1,66 1,45 1,24 1,98 1,78 1,57 1,36 1,15 1,79 1,61 1,42 1,24 1,06 1,55 1,39 1,24 1,08 0,93 Table 5-8 Lifetime maintenance costs for varying CMS performances for the robust failure rate partition 1,82 1,67 1,52 1,38 1,24 1,73 1,58 1,44 1,30 1,15 1,56 1,44 1,31 1,18 1,06 1,35 1,24 1,14 1,03 0,93

78 54 Figure 5-9 Lifetime gearbox maintenance costs for different as a function of for the robust failure rate partition Figure 5-10 Lifetime gearbox maintenance costs for different as a function of for the robust failure rate partition

79 Influence of the distance to shore As stated in section the wind farm distance is a parameter, which influences the maintenance costs of a wind turbine. In order to assess the influence of distance, the gearbox maintenance costs and the required CMS performances are estimated for close, middle and far offshore wind farms A wind farm is close to shore when the distance to shore is less than 50 km, middle between 50 km and 100 km to shore and far for distances over 100 km and below 150 km. Figure 5-11 shows the trend to build wind farms further away from the coast. Figure 5-11 Wind farms online, under construction and consented at different distances to shore [86] The distance influences the vessel renting days. With respect to a near shore wind farm, the difference in distance to shore by a repair or maintenance action for middle or a far offshore wind farm is simulated by respectively one and two extra vessel renting days. Secondly the probability of a weather window decreases if wind farms are located farther offshore. More remote locations require larger weather windows for repair actions, due to the longer travelling time. For this reason the accessibility of a far offshore wind farm is probably less than a wind farm near shore. This is simulated by an increase of the worst-case weather window waiting time. In case of a middle located wind farm the maximum weather window waiting time is 1.5 times the weather window waiting time for a near shore wind farm. The worst case weather window waiting time of a far offshore wind farm is multiplied with a factor 2, based on [85]. An assumption in this analysis is that all support vessels are harbour based. For visits a vessel and maintenance crew travels from the harbour to the site and back. For far offshore wind farms new maintenance strategies such as service substations at the wind farm or socalled floatels or mother-daughter vessels are under discussion as a way to increase wind farm availability and decrease maintenance costs. However, due to a lack of data, this is not taken into account in this analysis.

80 Without CMS The results of the cost simulations are shown in Table 5-9 and Figure As expected a wind turbine further offshore results in higher averaged gearbox maintenance costs. With respect to a wind turbine close to shore, a middle categorized wind turbine leads to an increase in average gearbox maintenance costs of around 22%. A far offshore-categorized wind farm will spend on average 44% more on gearbox maintenance than a wind farm close to shore. Also the deviations of the averaged cost increase with increasing distance to shore, making maintenance costs harder to predict. Table 5-9 Results of Monte Carlo Simulation for wind turbines at different distances offshore Distance to Mean costs [x10 6 ] Standard deviation [x10 6 ] shore Weak Medium Robust Weak Medium Robust Close 2,33 2,07 1,82 1,00 0,88 0,74 Middle 2,83 (+21%) 2,54 (+23%) 2,22 (+22%) 1,25 (+25%) 1,10 (+25%) 0,93 (+26%) Far 3,36 (+44%) 2,99 (+44%) 2,62 (+44%) 1,49 (+49%) 1,32 (+50%) 1,11 (+50%) Figure 5-12 Weibull distribution of the gearbox maintenance costs for different distances offshore With CMS To assess the influence of distance on the profitability of a CMS, additional MC simulations are performed. Again the robust case is chosen to be conservative, as this is the case with the highest demand of CMS performance. At first is implemented and the minimally required performance is estimated, as well as the profit that can be obtained for the different wind farm distances, assuming CMS lifetime

81 57 costs of Secondly the same is done for their combined benefit for a far offshore wind farm., after which a final section reveals CMS implemented to reduce large and catastrophic gearbox failures The model indicates that the CMS performance by for a middle and far offshore wind turbine are slightly lower to become profitable. Figure 5-13 visualizes this, which indicates a performance of suffices for a CMS installed in a wind turbine located at the middle distance to become profitable. The far offshore wind farm requires a CMS performance of days to become profitable. This reflects that for higher gearbox maintenance costs a lower CMS performance is demanded. Figure 5-13 CMS performance requirements of to become profitable Figure 5-14 displays that the profit linearly increases with and, due to the fact that large and catastrophic failures are most expensive for far offshore wind turbines, the profit is highest far offshore.

82 58 Figure 5-14 Profit sensitivity to distance to shore as a function of CMS implemented to reduce waiting downtime Figure 5-15 shows the desired prediction time needed to be profitable. As illustrated the difference the distance to shore is insignificant. The required performance by for all three is minimally two days prior to a failure. Figure 5-15 CMS performance requirements of to become profitable

83 59 The difference in wind farm distance becomes apparent for higher values of, as visualised by Figure This difference can be explained by the increase in weather window waiting time for a middle and especially far offshore wind turbine. The longer waiting time results in longer scheduling time required, and thus higher margins for profit by higher values of. Figure 5-16 CMS profit as a function of Combined benefit of CMS functions Figure 5-17 and Figure 5-18 are presented to show the combined benefit of both and for the lifetime maintenance costs of a wind turbine gearbox, respectively as a function of and. Figure 5-17 and Figure 5-18 also depicts the clear difference in gearbox maintenance costs of a near shore wind turbine (dotted lines) and the far offshore wind turbines (solid lines) for different values of and. This difference is thus caused by extra vessel costs and downtime by weather window waiting time.

84 60 Figure 5-17 Lifetime gearbox maintenance costs for a 6MW wind turbine near shore and far offshore for different values of as a function of Figure 5-18 Lifetime gearbox maintenance costs for a 6MW wind turbine near shore and far offshore for different values of as a function of 5.3 Influence of the gearbox failure rate As a final sensitivity study the gearbox failure rate is analysed and different failure rates are compared with the base case. A difference in gearbox failure rate, results in different probabilities of each failure to occur, based on Equation 4-2, Equation 4-3, Equation 4-4 and

85 61 Equation 4-5. The probabilities of each failure per category for different failure rates are given in Appendix F Without CMS Different gearbox failure rates significantly influences the maintenance costs as can be seen in Table 5-10 and Figure The difference in failure rate could be amongst others model, operation or location dependent and differs per wind farm as certain papers describe [7] [8] [9] [53] [81]. Table 5-10 Results of Monte Carlo Simulation for wind turbines with different annual failure rates Failure Mean costs [x10 6 ] Standard deviation [x10 6 ] rate Weak Medium Robust Weak Medium Robust 0,05 1,46 (-37%) 1,37 (-34%) 1,29 (-29%) 0,59 (-41%) 0,53 (-50%) 0,45 (-39%) 0,15 2,33 2,07 1,82 1,00 0,88 0,74 0,25 3,18 (+36%) 2,77 (+34%) 2,34 (+29%) 1,29 (+29%) 1,14 (+30%) 0,95 (+28%) Figure 5-19 Weibull distribution of the gearbox maintenance costs for different failure rates (Medium) The difference in failure rate results in large differences in lifetime gearbox maintenance costs. Table 5-10 reveals that a decrease in failure rate of 0,10 lead to gearbox maintenance cost reductions of 29% to 37%. Besides this staggering decrease in average gearbox maintenance costs, Figure 5-19 shows that a decrease in failure rate also significantly decreases the total maintenance cost uncertainty by the lower standard deviations. Scale and shape parameters of Figure 5-19 can be found in Appendix D.2.

86 With CMS To estimate the requirements for the CMS to become profitable and to assess the influence of gearbox failure rate on these requirements, additional simulations are executed and presented in this section, assuming CMS lifetime costs of CMS implemented to reduce large and catastrophic gearbox failures As shown by Figure 5-20 the desired performance of differs significantly for yearly failure rates varying from 0,05 to 0,25. For a gearbox with a high failure rate of 0,25, a detection efficiency of 3% already suffices. On the other hand a failure rate of 0,05, a detection efficiency of at least 13% is needed. A lower failure rate automatically leads to less large and catastrophic failures in the simulations. This means that there are fewer opportunities for the CMS to prevent a failure. This becomes even more apparent in Figure 5-21, where a difference in failure rate leads to profit differences of several tons for higher values of. Figure 5-20 CMS performance requirements of to become profitable for different failure rates

87 63 Figure 5-21 Profit sensitivity to failure rate as a function of CMS implemented to reduce waiting downtime As for, requirements differ for different failure rates, as indicated by Figure Once more the lowest failure rate of 0,05, demands the highest prediction time per failure, with for a profitable installation. Figure 5-22 CMS performance requirements of to become profitable

88 64 Figure 5-23 shows the profit by the CMS for higher values of costs of , assuming lifetime CMS Figure 5-23 Profit sensitivity to failure rate as a function of Combined benefit of CMS functions Finally both CMS performance parameters are combined, to show what their combined potential profit for the gearbox lifetime maintenance costs and how it is affected by failure rate. Figure 5-24 and Figure 5-25 clearly reveal that the lower failure rate results in significantly lower lifetime gearbox maintenance costs. However they also show that the CMS profit is higher for high failure rates by the clear difference in the slope of the plots with failure rate of 0.15 (dotted lines) and 0.05 (solid lines).

89 65 Figure 5-24 Lifetime maintenance costs for a wind turbine gearbox with a failure rate of 0.05 (solid lines) and 0.15 (dotted line) for different values of as a function of Figure 5-25 Lifetime maintenance costs for a wind turbine gearbox with a failure rate of 0.05 (solid lines) and 0.15 (dotted line) for different values of as a function of The results found in this chapter reveal the potential value a CMS could have on the gearbox maintenance costs over lifetime. Significant costs might be saved by a reduction in large and catastrophic failures or a reduction in waiting downtime. Certainly when lifetime maintenance

90 66 costs increase, due to more failure prone wind turbines or wind farms farther offshore, the potential of CMSs for lowering the lifetime maintenance costs increases. In order to outbalance the costs of a CMS by its revenue, this chapter shows that only low performance levels are needed. In an interview with Franklin Heinsen, Business Manager Condition Monitoring of SKF, no exact performance levels of the CMS could be given, but he claims that the performance levels, as found in this thesis, will be easily met by the current CMSs. This justifies from an economic point of view the implementation of gearbox CMSs for wind turbines offshore.

91 67 Chapter 6. Conclusions and discussion As the high costs of offshore wind energy are for a large share caused by maintenance costs over lifetime, the gearbox especially is considered as one the most expensive components. This is caused by high downtime in case of failure and the high-cost maintenance actions required after failure. A model is developed, using the Monte Carlo method, to estimate the lifetime gearbox maintenance costs for different case studies. Additionally the model is used to perform a cost-benefit analysis regarding Condition Monitoring Systems. In case a failure occurs, corrective maintenance has to be applied to restore the wind turbine to its functioning state. In the worst case scenario the entire gearbox needs to be replaced, resulting in dramatic costs. By the hand of a literature review this thesis presents the approximated costs of a gearbox replacement offshore as a function of the wind turbine rated power. These costs can run up to over one million euro per event for high wind turbine ratings considered for near future offshore wind farms. These costs are mainly caused by the high material costs of the gearbox, followed by high costs of the required marine systems and downtime. To prevent high maintenance costs CMSs might be a solution. By the data provided by sensors, specialized data-mining techniques are able to detect irregularities in the data obtained by the CMS, which can be a sign of an incipient failure in the future and setting off an alarm. By this alarm large failures and/or downtime could be prevented, significantly reducing the costs of maintenance. If a CMS is only used to reduce service and inspection visits, significant cost savings can already be made, certainly if we move farther offshore. Nevertheless such a system is even more profitable when it is able to prevent large gearbox failures and downtime as a result of spare part logistics, vessel mobilisation or weather window waiting time. To underlie this hypothesis this work shows the potential cost savings by a CMS by different simulations. 6.1 Conclusions The cost model is developed to determine the required performance of a gearbox CMS to become profitable in the wind turbine lifetime. Therefore the lifetime gearbox maintenance costs were estimated for a near shore 6 MW wind turbine without CMS. The simulation reveals lifetime gearbox maintenance costs of around two million euro. The high uncertainty of the costs during the wind turbine lifetime are reflected by the standard deviation (here 40% of the lifetime maintenance costs). In addition a CMS is added and the lifetime costs are compared, resulting in high potential cost savings by CMSs. High revenues were found, for CMSs capable of preventing large failures or downtime, that significantly outbalance the relatively small costs of gearbox CMSs. This is also reflected by the low CMS performance required to break-even. A second set of simulations showed that wind farm distance and failure rate have significant effects on the gearbox lifetime maintenance costs. A wind park farther away from the shore or for a failure prone wind turbine, the requirements for a CMS are less demanding.

92 68 The main conclusion of this thesis is that the installation of a CMS is already profitable at low performance levels, as lifetime CMS costs are very small compared to the potential revenue of such systems installed at wind turbine gearboxes offshore. In a discussion with a supplier of CMS (SKF WindCon) it was stated that these performance levels would easily be met by the current systems offered at the market. Therefore I dare to state that it is very well justified to implement condition monitoring systems to wind turbine gearboxes offshore. 6.2 Discussion The model developed in this work, estimates the lifetime costs of gearbox maintenance with and without CMS. These estimates are based on assumptions and data found in literature, which must be critically assessed. Especially the cost indications vary significantly with time, location and event and therefore costs for vessel renting could be too optimistic compared with real life. In my study I tried to estimate the costs as good as possible and support each choice by as many sources as available. However especially cost indications are not publicised easily by those involved. Nevertheless the assumptions made in this work might need further validation. For instance a single statement from industry that the majority of the replaced gearboxes can be refurbished, poorly founds my assumption that the gearbox can be refurbished in 75% of the cases. A second assumption that is made in this thesis is that every wind turbine offshore experiences at least one gearbox replacement. This is assumed to get the lifetime maintenance costs more in line with the estimations of infield industry. In the simulation the MC method uniformly distributes the component costs of large and small failures and the length of weather window waiting time. This means that the probability of occurrence of the maximum, minimum or mean is equal in the simulations, which might not be true. Certainly in case of weather window waiting time, a bell shape probability distribution might be more realistic and would improve the model accuracy. In future work the following could be considered: Further improvement of the model and validation of the results, as described above. By minor adaptations the model could also be used to assess the lifetime maintenance cost and the required CMS performance of other components, such as the generator or the wind turbine blades. A more detailed look at spare part and vessel logistics and the possible implementation of different access or supply stock strategies. For instance significant cost savings could be made, when repair actions of multiple wind turbines in a farm are planned and executed simultaneously (batch-wise maintenance). It would be interesting to find out what this does for the lifetime maintenance costs and subsequently the required minimum CMS performance.

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100 76

101 77 Appendices A. European inflation rates Here the inflation rates per year are listed, which are used to correct the gearbox replacement costs of various papers in order to properly compare them [59]. Table 11 Inflation rates Year Inflation rate Eurostat Index to 1 January ,19% ,65% ,47% ,35% ,21%

102 78

103 79 B. Maintenance costs estimation equations B.1. Total gearbox replacement costs Equation B-1 Total gearbox maintenance costs Where stands for the costs of the new gearbox, for the labour costs of the repair crew, for the costs for the access and lifting systems and finally for the loss of revenue made when the wind turbine does not produce electricity due to the gearbox defect and replacement. B.2. Component costs Equation B-2 Estimation of component costs Here stands for the size of the wind turbine in MW, p WT/MW the price of a turbine per MW (1.2 M$/MW [52] [87]) and finally gives the gearbox CAPEX share, which according to [88] lies around 12%. B.3. Labour costs Equation B-3 Estimation of labour costs Where stands for the number of technicians working, the number of days they are working, stands for the hours per day they work (8-12 hours) and finally stands for the hourly rate of each worker (70 /hour [54]). B.4. Access costs Equation B-4 Estimation of access costs Where stands for the day rate of a Crew Transporting Vehicle, stands for the number of days the CTV is needed. The same holds for the bigger lifting vessel. Prices for offshore vessels can be found in section and the number of days depends on several variables, e.g. wind farm distance, lifting operation and the weather conditions. According to Andrawus et al. a crane capable of hoisting a gearbox of a wind turbine on land has a day rate of However one must consider the fact that this is a 2007 price indication. B.5. Production loss Equation B-5 Estimation of costs made by production losses

104 80 The factor 24 stands for the number of hours per day, stands for the normal capacity factor of the wind turbine (set to 37.5% in this thesis, according to [89] [90]) and is the selling price of electricity, which is found to be [91].

105 81 C. M-files The cost model used for both the sensitivity analysis and cost-benefit analysis consists out of three sections. A main file, which run each simulation and in which the variables per simulation are defined. An input file, where all input parameters are defined, for instance the number of iterations, wind turbine lifetime and failure probability partition. The third file is the repair cost file, which determines the costs of each failure in different cases. The three files can be found hereunder. C.1. Main file % % clear all % % close all % % clc tic distr = 3; inputsmc Ccorr=zeros(N,1); rr=1; %for distr=1:3 %failure category respectively weak, medium and robust %for km = 20:50:120 %distance to shore %for FR = 0.05:0.1:0.25 %total gearbox failure rate %for CMScosts = 25000:15000:55000 %lifetime CMS costs %for delta=0:4 %prediction time deltat %for Dtime=1:4 %means deltat is 7, 31 or 92 days for CMSacc = 0:0.01:.10 %detection efficiency varying from 0 to 10% inputsmc for n = 1:N A=rand(L,1); repaircostsiv %calculation of number of gearbox replacements necessary in lifetime, total %loss % c1=0; c1ref=0; for i = 1:L if A(i)<F1*FR-(F1*FR)*CMSacc c1=c1+1; end %calculation of number of gearbox replacements necessary in lifetime which %can be refurbished % if A(i)<Pref*(F1*FR-(F1*FR)*CMSacc) c1ref = c1ref + 1; c1=c1-1; end end %calculation of number of large gearbox failures in lifetime % B=rand(L,1); c2=0; for i = 1:L

106 82 if B(i)<F2*FR-(F2*FR)*CMSacc %+(F1*FR/(1/CMSacc) c2=c2+1; end end %calculation of number of small gearbox failures in lifetime % D=rand(L,1); c3=0; for i = 1:L if D(i)<F3*FR+((F1+F2)*FR)*CMSacc c3=c3+1; end end %calculation of number of scheduled inspection visits in lifetime % E=rand(L,1); c4=0; for i = 1:L if E(i)<1; c4=c4+1; end end c1ref=c1ref+1; c = [c1 c2 c3 c4]; C1 = c1*(gearboxreplacement); C1ref = c1ref*(gearboxreplacementref); C2 = c2*(bigrepair); C3 = c3*(smallrepair); C4 = c4*scheduledvisit; Ccorr(n) = C1+C1ref+C2+C3+C4;% represents the total lifetime gearbox costs per iteration % if Ccorr(n) == 0 % Ccorr(n) = 0.1; % end end; format long meanccorr(rr) = mean(ccorr) %mean lifetime gearbox maintenance costs over N iterations standarddeviation(rr) = std(ccorr) %standard deviation of the lifetime gearbox maintenance costs over N iterations maxccorr(rr) = max(ccorr); PARMHAT = wblfit(ccorr); Scalefactor(rr) = PARMHAT(1); %gives the scale factor of the N iterations Shapefactor(rr) = PARMHAT(2); %gives the shape factor ot the N iterations % % % range = 0:maxCcorr; % % CMSacc; if meanccorr(rr) > meanccorr(1) meanccorr2(rr) = meanccorr(1); else meanccorr2(rr) = meanccorr(rr); end % % Profit by the implementation of a CMS:

107 83 CMSprofit(rr) = meanccorr(1) - meanccorr2(rr) - CMScosts; rr=rr+1; % wblstat(parmhat(1),parmhat(2)) % PDF_Ccorr = wblpdf(range,parmhat(1),parmhat(2)); % % % CDF_Ccorr = wblcdf(range,parmhat(1),parmhat(2)); % hold on % plot(0:maxccorr, PDF_Ccorr) % % xlabel('maintenance costs gearbox over lifetime ( )') % ylabel('probability of occurence') % axis([0 maxccorr]) end hold on CMS = 0:1:10; plot(cms,cmsprofit,'-or','linewidth',2, 'MarkerSize',2) %plot(cms,meanccorr,'-o','linewidth',2, 'MarkerSize',2) hold on %legends used in the work %legend('dtcms=0','dtcms=7','dtcms=31','dtcms=92') %legend('dtcms=0 FR=0.15','dTcms=7 FR=0.15','dTcms=31 FR=0.15','dTcms=92 FR=0.15','dTcms=0 FR=0.05','dTcms=7 FR=0.05','dTcms=31 FR=0.05','dTcms=92 FR=0.05') %legend('dtcms=0 near shore','dtcms=7 near shore','dtcms=31 near shore','dtcms=92 near shore','dtcms=0 far offshore','dtcms=7 far offshore','dtcms=31 far offshore','dtcms=92 far offshore') %legend('eta CMS=0','eta CMS=50%','eta CMS=100%','fontsize',24) %legend('eta CMS=0','eta CMS=25','eta CMS=50%''eta CMS=75%','eta CMS=100%','fontsize',24) %legend('eta CMS=0 nearshore','eta CMS=50% near shore','eta CMS=100% near shore','eta CMS=0 far offshore','eta CMS=50% far offshore','eta CMS=100% far offshore') %legend('eta CMS=0 FR=0.15','eta CMS=50% FR=0.15','eta CMS=100% FR=0.15','eta CMS=0 FR=0.05','eta CMS=50% FR=0.05','eta CMS=100% FR=0.05') %legend('close to shore','middle distance','far offshore') legend('0.05 failures a year','0.15 failures a year','0.25 failures a year') %legend('cms costs = ','CMS costs = ','CMS costs = ') %legend('weak', 'Medium', 'Robust') %legend('75% WW waiting time','100% WW waiting time','125% WW waiting time') %area(cms,cmsrevenue) %labels used in the work xlabel(' eta CMS (%)','fontsize',16) %xlabel('dt CMS (days)','fontsize',16) %ylabel('profit due to delta CMS ( )','fontsize',16) ylabel('profit due to eta CMS ( )','fontsize',16) %ylabel('lifetime gearbox maintenance costs ( )','fontsize',16) %xlabel('eta','fontsize',16) %ylabel(' delta ','fontsize',16) rr = 1 toc % represents the line which divides the field of positive and negative % profit:

108 84 grid on z=zeros(length(cms)); plot(cms,z,'--k','linewidth',2) beep on; beep C.2. Repair costs file %this file first determines the waiting downtime for different distances %to shore, hereafter the costs of each failure can be calculated. This is %done for every iteration if km < 50 downtimescheduling1 = (rand(1)*(147))-delta;%(dtime); if downtimescheduling1 < 0 downtimescheduling1 = 0; end downtimescheduling2 = (rand(1)*(90))-delta;%(dtime); if downtimescheduling2 < 0 downtimescheduling2 = 0; end downtimescheduling3 = (rand(1)*(15))-delta;%(dtime); if downtimescheduling3 < 0 downtimescheduling3 = 0; end end if 50 < km & km < 100 downtimescheduling1 = (rand(1)*(147*1.5))-delta;%(dtime); if downtimescheduling1 < 0 downtimescheduling1 = 0; end downtimescheduling2 = (rand(1)*(90*1.5))-delta;%(dtime); if downtimescheduling2 < 0 downtimescheduling2 = 0; end downtimescheduling3 = (rand(1)*(15*1.5))-delta;%(dtime); if downtimescheduling3 < 0 downtimescheduling3 = 0; end end if km>100 downtimescheduling1 = (rand(1)*(147*2))-delta;%(dtime); if downtimescheduling1 < 0 downtimescheduling1 = 0; end downtimescheduling2 = (rand(1)*(90*2))-delta;%(dtime); if downtimescheduling2 < 0 downtimescheduling2 = 0; end downtimescheduling3 = (rand(1)*(15*2))-delta;%(dtime); if downtimescheduling3 < 0 downtimescheduling3 = 0; end end

109 85 downtimec1 = downtimec*(3+downtimescheduling1); downtimec2 = downtimec*(2+downtimescheduling2); downtimec3 = downtimec*(1+downtimescheduling3); MatCostBig = rand(1)*300000; %material costs of a big repair action MatCostSmall= rand(1)*50000; %material costs of a small repair action Gearboxrefurbishsavings = rand(1)*200000;%savings that are made if the gearbox can be reused after refurbishing and replacements if km <50 gearboxreplacement = GBR + downtimec1; gearboxreplacementref = GBR + downtimec1 - Gearboxrefurbishsavings; %gearbox can be refurbished and reused, saving bigrepair = downtimec2 + MatCostBig + 2*Liftboat; smallrepair = downtimec3 + MatCostSmall + CTV; scheduledvisit = 1/6*downtimeC ; elseif 50 < km & km < 100 gearboxreplacement = GBR + downtimec1 + downtimec + JumpingJack ; gearboxreplacementref = GBR + downtimec1 + downtimec+ JumpingJack - Gearboxrefurbishsavings; bigrepair = downtimec2 + downtimec + MatCostBig + 3*Liftboat; smallrepair = downtimec3 + downtimec + MatCostSmall + 2*CTV; scheduledvisit = 1/6*downtimeC ; else gearboxreplacement = GBR + downtimec1 + 2*downtimeC + 2*JumpingJack; gearboxreplacementref = GBR + downtimec1 + 2*downtimeC + 2*JumpingJack - Gearboxrefurbishsavings; bigrepair = downtimec2 + MatCostBig + 2*downtimeC + 4*Liftboat; smallrepair = downtimec3 + 2*downtimeC + MatCostSmall + 3*CTV; scheduledvisit = 1/6*downtimeC ; end C.3. Input file Turbinesize = 6; %Rated power wind turbine in MWs N = ; %Number of simulations L = 20; %Lifetime wind turbine FR = 0.15; %Gearbox failure rate km = 30; %Wind turbine distance to shore %distr=2 %CMSacc = 0;%1;%1;%0.75;%CMS efficiency delta = 0; % delta(1) = 0; % delta(2) = 7; % delta(3) = 31; % delta(4) = 92; CMScosts = 25000; Pref = 0.75 ; %this is the probability that after a gearbox replacement the gearbox can be refurbished and reused, saving euro %distr = 2; %1 == Weak; 2 == Medium; 3 == Robust if distr == 1 F1 = 0.17; %Share of catastrophic gearbox failures and gearbox needs to be replaced F2 = 0.45; %Share of big gearbox repair action need F3 = 0.38; %Share of small gearbox repair action

110 86 S = 20; end if distr == 2 F1 = 0.13; F2 = 0.35; F3 = 0.52; S = 20; end if distr == 3 F1 = 0.09; F2 = 0.24; F3 = 0.66; S = 20; end %Number of service visits in lifetime %Share of catastrophic gearbox failures and gearbox needs to be replaced %Share of big gearbox repair action need %Share of small gearbox repair action %Number of service visits in lifetime %Share of catastrophic gearbox failures and gearbox needs to be replaced %Share of big gearbox repair action need %Share of small gearbox repair action %Number of service visits in lifetime GBR = 13065*Turbinesize.^ *Turbinesize ; JumpingJack = 74040; %average price of rental of one jumping jack and two CTV including 5040 labour costs per day Liftboat = 43040; %average price of rental of one liftboat and one CTV including 5040 labour costs per day CTV = 5360; %average price of rental of one CTV including 3360 labour costs per day (4 workers instead of 6) downtimec = Turbinesize*1000*24*0.067*0.375; %cost of production loss of one day due to downtime

111 87 D. Mean, standard deviation and Weibull parameters D.1. Distance to shore: Table D-1 Weibull parameters, standard deviation and mean of the gearbox lifetime costs for different failure partitions Distance to shore: Weak failure partition Scale factor Shape factor STD Mean Near shore ,24 2, , ,73 Middle ,87 2, , ,94 Far offshore ,55 2, , ,05 Distance to shore: Medium failure partition Scale factor Shape factor STD Mean Near shore ,04 2, , ,93 Middle ,09 2, , ,98 Far offshore ,03 2, , ,09 Distance to shore: Robust failure partition Scale factor Shape factor STD Mean Near shore ,66 2, , ,44 Middle ,85 2, , ,64 Far offshore ,43 2, , ,26 D.2. Failure rate Table D-2 Weibull parameters, standard deviation and mean of the gearbox lifetime costs for different failure partitions and different gearbox failure rates Failure rate: Weak failure partition Scale factor Shape factor STD Mean 0, ,83 2, , ,78 0, ,24 2, , ,73 0, ,88 2, , ,58

112 88 Failure rate: Medium failure partition Scale factor Shape factor STD Mean 0, ,94 2, , ,83 0, ,04 2, , ,93 0, ,65 2, , ,43 Failure rate: Robust failure partition Scale factor Shape factor STD Mean 0, ,56 2, , ,26 0, ,66 2, , ,44 0, ,77 2, , ,07 D.3. For different percentages of probability reduction of large and catastrophic failures Table D-3 Weibull parameters, standard deviation and mean of the gearbox lifetime costs for different CMS performances with respect to reduced probability of large and catastrophic failures : Weak failure partition Scale factor Shape factor STD Mean 25% ,66 2, , ,71 50% ,89 2, , ,43 75% ,87 2, , ,58 100% ,27 6, , ,59 : Medium failure partition Scale factor Shape factor STD Mean 25% ,59 2, , ,45 50% ,39 2, , ,39 75% ,11 2, , ,70 100% ,40 6, , ,51 : Robust failure partition Scale factor Shape factor STD Mean 25% ,76 2, , ,15 50% ,25 2, , ,64 75% ,93 3, , ,97 100% ,35 6, , ,64

113 89 D.4. For different values of prediction time Table D-4 Weibull parameters, standard deviation and mean of the gearbox lifetime costs for different failure partitions and CMS performance with respect to prediction time : Weak failure partition Scale factor Shape factor STD Mean ,70 2, , , ,67 2, , , ,56 2, , ,91 : Medium failure partition Scale factor Shape factor STD Mean ,36 2, , , ,89 2, , , ,29 2, , ,16 : Robust failure partition Scale factor Shape factor STD Mean ,57 2, , , ,63 2, , , ,64 2, , ,33

114 90

115 91 E. Commercial Wind Turbine CMS for the gearbox Table E-1 Survey of Commercial Wind Turbine CMS for the gearbox found by Yang et al. (Yang, Tavner, Crabtree, Feng, & Qiu, 2012) No. Name Product information Product Company Major functions Notes 1 WindCon 3.0 SKF (Sweden) Collect, analyse and compile data that can be configured to suit management, operators and maintenance engineers. The system focuses on the condition monitoring of amongst others WT gearbox by the combined use of vibration transducers and a lube oil debris 2 TCM Gram & Juhl (Denmark) 3 WP4086 Mita-Teknik (Denmark) 4 Brüel&Kjӕr Vibro Brüel & Kjӕr (Denmark) Advanced signal analysis on vibration, vibro-acoustic, and strain combined with automation rules and algorithms for generating references and alarms. Integrated with WT SCADA, the system provides real time frequency and time domain turbine operational signals and gives off analyses of alarms based on predefined thresholds Collect and process data at fixed intervals and remotely send results to diagnostic server. The time waveform of the data at any time is accessible for further analysis. 5 CBM GE Bently (USA) The system gives monitoring and diagnosis of drive train parameters. Correlate CM signals with WT operational information (e.g. wind and shaft rotating speeds), and give off alarms via SCADA 6 CMS Nordex (Germany) Actual vibration values during WT start-up period are compared with the reference counter The WT gearbox is monitored by using spectral analysis methods. With the aid of eight accelerometers the WT gearbox is monitored by using both time and frequency domain analysis techniques. The WT gearbox is monitored by the approach of vibration analysis in combination with temperature and acoustic analyses. The vibrations of WT gearbox and oil temperatures are monitored The system focuses on the monitoring of main bearing, gearbox, and

116 92 7 SMP-8C GamesaEolica (Spain) 8 PCM200 Pall Europe Ltd (UK) 9 TechAlert 10/20 MACOM (UK) 10 MDS Wind VULKAN MDSWIND_T SEACOM (Germany) 11 Ascent Commtest (New Zealand 12 Condition Diagnostics System Winergy (Germany) values. Some Nordex turbines also use the Moog Insensys Fibre Optic measurement system. Continuous online analyses WT gearbox and comparison of the spectral trends. Warnings and alarms are given through the wind farm management system. This is a real time system for testing and assessing fluid cleanliness. TechAlert 10 is an inductive sensor to count and size the ferrous and nonferrous debris, whereas TechAlert 20 is a sensor only for counting ferrous particles. MDSWind measures the vibrations of the gearbox and calculates and display the statistic indices (e.g. RMS, Crest factor) online. Ascent is a vibrations analysis system for monitoring the gearbox by the approach of spectral analyses and time domain statistics. The system analyses vibrations, load and oil to give diagnosis, predict and recommend for corrective action. Automatic fault identification is provided. Pitch, yaw and inverter monitoring can also be integrated into the system. 13 OneProd Wind Areva (France) The system monitors the gearbox by measuring the oil debris, structure and generator. WT gearbox is online monitored through the spectral analyses of its vibrations. The cleanliness of gearbox lubrication oil is monitored. Both systems are designed for monitoring the debris containing in lubrication or other circulating oils. MDSWIND-T is a four channel portable system developed based on MDSWind. System available in three complexity levels. Level 3 includes frequency band alarms, machine template creation, and statistical alarming. It mainly focuses on health monitoring of the gearbox through vibration analysis and oil debris counter. It consists of operating condition channels to trigger data acquisitions,

117 93 14 WinTControl Flender Service GmbH (Germany) 15 WiPro FAG Industrial Services GmbH (Germany) 16 HYDACLab HYDAC Filtertechnik GmbH (Germany) 17 Oil Condition Monitoring System 18 Gearbox oil condition monitoring Rexroth Bosch Group (Germany) Intertek (UK) shaft displacement, and electrical signals. It is a vibration monitoring system for assessing the health condition of a WT gearbox. Both time and frequency domain analyses are adopted. Temperature and vibration measurements are taken for monitoring the gearbox. It is a system for monitoring the particles (including air bubbles) in hydraulic and lubrication systems. It is a system for the early detection of gearbox damages and the monitoring of oil cleanliness. High dissolving sensors for the measurement of particles and water content in the lubricating oil are available. Both permit an estimate of the remaining life time of the lubricating oil. Intertek oil condition monitoring services include testing of gearbox oils and lubricants, helping clients extend runtimes for expensive turbines, windmills and other equipment while minimizing downtime and costly repairs. measurement channels for surveillance and diagnosis, optional additional channels for extended monitoring. Vibration measurements are taken when load and speed trigger are realized. Time frequency analysis used in the system allows speeddependent frequency band tracking and speed-variable alarm level. It is mainly used for monitoring of the WT gearbox. This system not only improves the reliability of WTs but also the efficient operation by predictable maintenance. This is an offline oil analysis system. 19 Icount System and IcountPD particle Detector Parker (Finland) Parker s system is an all-in-one system to determine whether or not system oil is IcountPD is a particle detector, whereas Icount system provides early warning

118 94 contaminated and the best way to detect particles online or offline. of any unwanted changes in hydraulic or lubrication oil quality. Thus, increasing the availability of the machinery by reducing the need for unnecessary downtime.

119 95 F. Probability of failure for different failure rates F.1. Probability of a gearbox replacement total loss Table F-1 Probability of failure F 1 for different failure rates and different failure rate partition categories Failures per year Weak: 17% Medium: 13% Robust: 9% ,21% 0,16% 0,11% ,43% 0,33% 0,23% ,64% 0,49% 0,34% ,85% 0,65% 0,45% ,06% 0,81% 0,56% F.2. Probability of a gearbox replacement, which can be refurbished in workshop Table F-2 Probability of failure F 1 for different failure rates and different failure rate partition categories Failures per year Weak: 17% Medium: 13% Robust: 9% ,64% 0,49% 0,34% ,28% 0,98% 0,68% ,91% 1,46% 1,01% ,55% 1,95% 1,35% ,19% 2,44% 1,69% F.3. Probability of a large gearbox failure Table F-3 Probability of failure F 2 for different failure rates and different failure rate partition categories Failures per year Weak: 45% Medium: 35% Robust:24% ,25% 1,75% 1,20% ,50% 3,50% 2,40% ,75% 5,25% 3,60% ,00% 7,00% 4,80% ,25% 8,75% 6,00% F.4. Probability of a small gearbox failure Table F-4 Probability of failure F 3 for different failure rates and different failure rate partition categories Failures per year Weak: 37% Medium: 51% Robust: 66% ,85% 2,55% 3,30% 3,70% 5,10% 6,60% 5,55% 7,65% 9,90% 7,40% 7,65% 13,20% 9,25% 10,20% 16,50%

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