Thermal Modeling and Analysis of a Wind Turbine Generator Authors: Dr Bogi Bech Jensen (Associate Professor) Technical University of Denmark Mathew Lee Henriksen (PhD student) Technical University of Denmark Mohsen Hosseini (PhD student) Technical University of Denmark Stig Högberg (MSc Student) Technical University of Denmark
Introduction The results of the research project named Thermal Modeling and Analysis of a Wind Turbine Generator are in this report recapitulated. This will fulfill the requirements stated in the contract agreement between Energinet.dk and Technical University of Denmark, DTU Electrical Engineering through the ForskEl programme. Fault detection in wind turbines is not a trivial task, but a rather complex one that throughout the years has developed from the method of reactive maintenance to condition-based maintenance. This calls for accurate, economical and reliable monitoring systems that can predict when a component or subassembly is failing, alerting the operator whom will then take action. The need for this type of maintenance comes with the recent increase of offshore wind turbines in which the 20-year lifetime O&M costs have increased from approximately 10-15 % for onshore turbines to 20-25 % for offshore (1). This has provided motivation to rethink the method of wind turbine maintenance, and many different techniques for monitoring both electrical and mechanical health of the wind turbine subassemblies have been developed. Of such can be mentioned gearbox vibration monitoring (2), electric power data analysis (3), (4) and (5), surveillance of metal debris concentration in lubrication oil system (6) and thermal monitoring of generator winding through embedded thermocouples (7). Very little literature was found on using thermal imaging in a condition monitoring system as a method to obtain the internal temperature of a wind turbine generator. Since the technology recently has dropped in price it was in this project proposed to investigate this technique and identify the main challenges. The more conventional methods for temperature monitoring, including lumped parameter thermal network models and FEM (finite element method) were developed as well. The work thus spreads from extensive literature reviews to generator tests and model development to on-site research in a Siemens 3.6 MW wind turbine. Steady state and transient thermal models of a test generator have been developed including both lumped parameter thermal network and FEM. These models have been validated against the existing prototype. Usage of a thermal imaging device in a wind turbine to monitor the subassemblies as part of a condition monitoring scheme has been investigated. A FLIR A320 was acquired for this and field tests along with literature reviews and the data from the analytical and numerical models served as foundation to draw general conclusions for further investigation. This project has been completed in close collaboration with Dong Energy who have contributed both by allowing access to their wind turbines as well as advising from their experience. The field of condition monitoring is one that Dong Energy takes very seriously and welcomes any new research that they possibly could benefit from.
Process Students have been involved in this project from the start. A brief description of the different student projects is given here: Bjarke Nordentoft Madsen (MSc) has investigated to what extend condition monitoring through power measurements on the wind turbine generator can replace some of the existing condition monitoring technologies, such as gearbox bearing vibration sensors. A test rig was used for this purpose, where bearing failures were introduced and identified based on power measurements. Alexandros Skrimpas (MSc) worked on identifying the main challenges associated with using a thermal camera inside a wind turbine. No information was found that suggested that this had been done before. A visit to a 3.6 MW wind turbine was arranged in collaboration with Dong Energy where on-site testing of the concept was performed. Stig Högberg (MSc) was involved in a literature review of condition monitoring with special focus on thermal monitoring, but also investigating the current trends within condition monitoring such as the power signal analysis using suitable algorithms. The work included meetings with Dong Energy and visits to one of their wind turbines. Mohsen Hosseini (Phd) was employed to develop analytical and numerical thermal models of a toroidally wound induction motor. This work included both steady-state and transient modeling of the motor as well as actual testing of it. Matthew Lee Henriksen (Phd) continued and finished the work started by Mr. Hosseini. Bogi Bech Jensen has initiated and been involved in all of the above. In addition to the above mentioned projects the authors are also working on publishing this work initially as two papers: one journal paper intended for IEEE Transactions and one conference paper, both concerning condition monitoring in wind turbine systems using advanced thermal modeling. The method of thermal imaging as a condition monitoring system was investigated thoroughly in collaboration with Dong Energy who allowed access to one of the Siemens 3.6 MW test wind turbines at Avedøre Holme. Here it was possible to inspect the nacelle interior and perform test monitoring with the newly purchased infrared Camera FLIR A320. One of the key issues of using a thermal camera in a wind turbine nacelle is accessibility to the components of interest. This emphasized the need to localize the areas of importance and perhaps construct an automated method to allow one single camera to cover more than one component or a larger area of it. A large part of work in this project has been concerned with thermal models of the toroidally wound induction motor. The test setup for temperature surveillance of the motor is shown in Figure 1. On this both steady state and transient tests were carried out.
Figure 1 Test setup for thermal measurement
Results Thermal models were developed for an axial flux motor that is built with a great number of embedded thermocouples, easing the verification process. Both FEM and thermal network models were created for both steady state and transient operation. Thermal Network Model One well-known method of thermal modeling is the usage of a lumped parameter thermal equivalent circuit (TEC). A TEC models the heat transfer throughout an electrical machine in a manner familiar to electrical engineers, where the losses take the role of current, temperature acts as voltage, and heat transfer paths are modeled as thermal resistances. If a transient model is required, thermal capacitances can be included. Advantages of a TEC include computational speed and flexibility. The number of nodes included in the TEC is chosen based on the degree of accuracy desired. Inclusion of more nodes allows the temperature to be predicted in more locations, but this adds complexity to the model. Simplifications such as neglecting to model the heat transfer in one or more paths are common, but must be justified. The symmetry of an electrical machine allows a portion of the machine to suitably represent the temperature throughout the whole machine. A TEC has been developed for the torroidally-wound induction machine prototype, in order to compare with the results from finite element analysis. The developed model considers one slot of the machine. The case used for comparison is that of 0.6A DC current. Copper losses at 0.6A are calculated based on the coil dimensions and the conductivity as a function of temperature, which has been determined experimentally. These are then injected into four positions throughout the coil with direct correspondence to their volumes: the portion of the coil in the top slot, the portion in the bottom slot, and the two sides.
Figure 2 A portion of the TEC is shown. Losses corresponding to the top, slotted portion of a single coil are injected here, and transferred to the stator core by thermal conduction.
Temperature [ C] 90 Average Coil Temperature 80 70 60 50 40 30 20 0 100 200 300 400 500 600 700 Time [min] Figure 3 Results of TEC transient analysis of the toroidally wound induction motor with 0.6A Figure 5 displays the transient winding temperature obtained by the TEC. The steady-state temperature corresponds closely to the experimental and finite element results. The temperature rise appears to occur faster than what was found with the other methods. One possible reason for this is the choice of not modeling the internal air and therefore missing its thermal capacitance. Also the estimation of the mass was performed by considering simplified geometry, disregarding some ridges, protrusions, etc. Therefore the masses tend to be estimated as a little smaller than they actually are Finite Element Analysis The FEM modeling was done in Infolytica ThermNet in 3D. 2D was attempted as well, but the magnetic path and unique construction of this particular motor made this difficult. The 3D model has been found to accurately predict the internal temperatures for a given load current. The heat distribution in a
quarter section of the motor for a steady state simulation is shown in Figure 4. Figure 4 Steady state temperatures with 0.6 A
The simulated transient winding temperature is plotted in Figure 5 along with the experimental response, and the simulated transient temperature is observed to be in good agreement with that found from experimental test. The TEC response is also plotted and the mentioned overshoot of temperature is observed. Figure 5 Temperature transient of the winding: FEA, experimental and TEC included The FEA model has been tested by simulating several different currents and operating modes (AC and DC). The above simulation is a for a DC current of 0.6 A. Thermal Camera Through literature studies and on-site test in a Siemens 3.6 MW wind turbine the main challenges that arise when using thermal imaging cameras for condition monitoring in wind turbines were identified. A non-calibrated infrared image of the gearbox and bearings taken in the wind turbine is depicted in Figure 6. Temperature measurements from a thermal camera are not trivially interpreted since numerous sources of inaccuracies exist and failing to identify and adjust for these will lead to inaccurate readings that ultimately might have catastrophic consequences.
Figure 6 Overview of the gearbox and the bearings In the following numbered list a brief description of the main challenges associated with using a thermal camera for condition monitoring in wind turbines is given. 1. To ensure accurate surface temperature measurements, the emissivity of the surface must be known and adjusted for. Failing in this will result in inaccurate readings. 2. Models that relate the surface temperature to that of the inside must be developed for each component being monitored. 3. Subassemblies suitable for monitoring with a thermal camera must be identified, and a stationary monitoring setup must be made. This could be constructed as a rail on which the camera could slide and several subassemblies could be monitored with one camera. Monitoring the generator by this method was found to be very challenging since it is enclosed in an aircooling tank and was thus inaccessible. The camera records the temperature on a surface and models are needed to translate this temperature to internal ones. If a camera were successfully installed inside the cooling box of the generator, one could expect very high gradient temperatures that call for very accurate models.
Potential / future work Using thermal imaging as a condition monitoring system in wind turbines is a technology that needs more investigation in order to mature, but it holds a unique advantage in its potential to reflect the internal hotspots by using the surface temperature along with advanced models of the thermal characteristic of the device. This approach is currently expensive due to the cost of thermal imaging cameras. It is also difficult to install such cameras in a nacelle if the intention is to monitor the wind turbine generator, as these are commonly equipped with cooling ducts which would shield the generator temperature from the thermal imaging camera. Using temperature surveillance as condition monitoring has been discussed only little in the literature of condition monitoring. It has been argued that using one or more thermal cameras as a condition monitoring system in wind turbines could prove difficult due to several challenges. Some of these might be resolved by combining the different methods of temperature monitoring discussed in this project. Thermal finite element analysis is as demonstrated in the result sections capable of predicting the temperature of the motor for a given load current. Using this in a condition monitoring system could yield very precise results. The main disadvantage of FEA is the time it takes to solve a problem. 3D solutions in particular would require too much computational power for it to be cost-effective. Precalculated solutions for different load conditions might serve as a look-up table, eliminating the solving process. Successful 2D thermal models may be possible for machines of a more conventional construction, but these may still not be fast enough for real-time measurements. Thermocouples can be used as an inexpensive way of detecting the actual temperature of one or more specific spots in the generator. Most wind turbine generators are from the manufacturer delivered with a number of embedded thermocouples installed. Each of these will provide the operator with the exact temperature of the specific spot in which they are located. Temperature measurement with this method is cheap since the sensors are already installed, but additional thermocouples are not easily added and the operator will only be aware of the local temperatures. Combining an inexpensive temperature measuring technique, such as thermocouples, with computationally inexpensive modeling techniques is a promising area of research in the area of condition monitoring. If the spot temperature is measured for a number of locations in the generator, the thermal hotspots can be identified using models that relate the measured temperature to the rest of the machine. The computationally inexpensive analytical models developed in this project, need to be refined in order to be as accurate as the finite element models. If this can be achieved in the future then these models can be combined with thermocouples and form the basis of an inexpensive condition monitoring system. It is therefore proposed that research in the area of condition monitoring based on thermal measurements should continue, but that the focus should be on inexpensive measurement techniques combined with computationally inexpensive processing techniques.
Bibliography 1. A review of recent advances in wind turbine condition monitoring and fault diagnosis. Lu, B., et al. 2009, Power Electronics and Machines in Wind Applications, pp. 1-7. 2. An Integrated Fault Diagnosis Method of Gearboxes Using Oil Analysis and Vibration Analysis. Gao, Jingwei, et al. s.l. : International Conference on Electronic Measurement and Instruments, 2007. 3. Condition monitoring and fault diagnosis of a wind turbine with a synchronous generator using wavelet transforms. Yang, Wenxian, Tavner, P.J. and Wilkinson, M. s.l. : 4th IET Conference on Power Electronics, Machines and Drives, 2008. 4. Condition monitoring and fault diagnosis of a wind turbine synchronous generator drive train. Yang, W., Tavner, P.J. and Wilkinson, M.R. s.l. : Renewable Power Generation, IET, 2009. 5. Cost-Effective Condition Monitoring for Wind Turbines. Yang, Wenxian, et al. s.l. : IEEE Transactions on Industrial Electronics, 2010. 6. Oil debris signal analysis based on empirical mode decomposition for machinery condition monitoring. Bozchalooi, I.S. and Liang, Ming. s.l. : American Control Conference, 2009. 7. Wind Turbine Generator Condition Monitoring Using Temperature Trend Analysis. Guo, P., Infield, D. and Yang, X. s.l. : IEEE Transactions on Sustainable Energy, 2011.