Design of a Photovoltaic Data Monitoring System and Performance Analysis of the 56 kw the Murdoch University Library Photovoltaic System

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1 School of Engineering and Information Technology ENG460 Engineering Thesis Design of a Photovoltaic Data Monitoring System and Performance Analysis of the 56 kw the Murdoch University Library Photovoltaic System A report submitted to the School of Engineering and Information Technology, Murdoch University in partial fulfilment of the requirements for the degree of Bachelor of Engineering 2013 Mathew De Cerff Unit Coordinator: Dr. Gareth Lee Supervisor: Dr. Martina Calais

2 Abstract This paper discusses the design of a data monitoring system, for a PV system which would enable the calculation of the performance ratio and a.c energy efficiency of a PV system which the data monitoring system would be installed to. The requirement of this system was to be at low cost and with reasonable accuracy. The final design of the data monitoring system consists of a total of five sensors, this includes a silicon pyranometer, energy meter, module temperature sensor, a.c voltage sensor and a.c current sensor. The total cost of producing this system was $ This paper also discusses the performance analysis of the 56 kw Murdoch University Photovoltaic System. The time period of this analysis is from June 2011 to August It was found that over the 27 month time period of the analysis the total system generation was MWh of electricity. It was seen in January 2013 a peak in monthly out of MWh for this month, and it was seen in June 2012 the system production was at its lowest generating 3.43 MWh. It was established that the sub array which had the best production was sub array 2 generating a total of MWh which is % of total production, and the worst producing sub array was sub array 3 generating a total of MWh which is 9.85 % of total production. The best yield factor month was in January 2013 producing a monthly average of kwh/kwp, and the worst yield factor month was June 2012 producing a monthly average of 61.70kWh/kWp. The overall sub array monthly average yield factor for this 27 month period was kwh/kwp, and the poly- crystalline modules and mono- crystalline modules produced a monthly average yield factor of kwh/kwp and kwh/kwp, respectively. It was established that the performance ratio of the overall system, poly- crystalline modules and mono- crystalline modules were 0.724, and 0.716, respectively. The a.c energy efficiencies of the overall system, poly- crystalline modules and mono- crystalline modules were 10.25%, 10.29% and 10.22%, respectively. This shows that the polycrystalline modules were the better performing photovoltaic technology. ii

3 Acknowledgements I would like to thank my supervisor Dr Martina Calais for her instrumental guidance and assistance in my thesis and university studies. Throughout this project I relied on the guidance of the following individuals: - Dr David Parlevliet for his assistance is retrieving the data using in this thesis. - Dr Trevor Pryor for his assistant in converting solar radiation data. - Jon Lockwood from One Temp for his assistance in the required components needed in the data monitoring system design. Finally, I am greatly thankful to Murdoch University and my family and friends for the continual support throughout my studies. iii

4 Contents Abstract... ii Acknowledgements... iii Figures... vi Tables... vii 1. Introduction Project Objectives Scope of Work Literature Review Background Murdoch University Photovoltaic System PV Modules Inverter Leeming Photovoltaic System PV module Inverter Design of Data Monitoring and Data Acquisition System Requirement Criteria Selection Criteria Design Process and Methodology Selection for cost and accuracy comparison Final Selections Design Development Final DMS Design Solar Radiation Sensor Temperature Sensor AC Energy Sensor AC Voltage and Current Sensors HOBO H Energy Logger Discussion of the DMS Performance Analysis Performance Analysis Method Final Yield Reference Yield iv

5 Performance Ratio System AC Energy Efficiency Performance Analysis Results of MULPVS Solar Radiation Method for Solar Radiation Data Solar Radiation Data Trend System Yields June to December of 2011 System Yields January to December 2012 System Yields January to August 2013 System Yields Overall System Yield System Yield Discussion Performance Ratio System Performance Sub Array Performance Performance Ratio Discussion Shading System AC Energy Efficiency Comparing PV Technologies Future Works Conclusion References Appendix Appendix A Design Specification Appendix B Solar Pathfinder Images Appendix C Sunny SensorBox Data Retrieval Appendix D Performance Results v

6 Figures Figure 1 - MULPVS Layout (Adapted from Stephen Rose thesis [3])... 5 Figure 2- Leeming PV System... 7 Figure 3- Diagram of DMS Design (For the purpose of observing the temperature sensor the component is place above the array which in actual fact would be under the solar panels.) Figure 4 - Incident Radiation on Plane of Array for Murdoch (Slope: 23 ) Figure 5 - Monthly Solar Radiation and Monthly System Output over Time Figure 6 -Monthly Solar Radiation vs. System AC Energy Efficiency over Time Figure Monthly Sub-Array Energy Production Figure Monthly Sub Array Energy Production Figure Monthly Sub Array Energy Production Figure 10 - Overall System Output Figure 11 - Monthly Average Yield Factor per Year (kwh/kwp) Figure 12- Monthly System Performance Ratio Figure 13 - Monthly Sub Array Performance Ratio Figure 14 - Shading Effects on Sub Arrays (January) Figure 15 - Shading Effects on Sub-arrays 1-4 Output in June Figure 16 - Shading Effects on Sub-arrays 5 and 6 Output in June Figure 17 - Shading Effects on Sub-arrays 7, 8 and 9 Outputs in June vi

7 Tables Table 1 - KD135GH-2P Specifications [8]... 6 Table 2- SG-175M5 Specification [9]... 6 Table 3- SMA SMC 6000A Specifications [10]... 7 Table 4 - Sun Rise 190W Specification[11]... 8 Table 5 - SMA SB1100 Specifications[12]... 8 Table 6 - SMA Webbox Options 1 and Table 7- SMA Option Table 8 - Onset Basic System[13, 19, 20] Table 9 - Onset Final System Components[13, 17, 19, 20, 22-24] Table 10 - IEC Measurement Accuracies [5] Table 11 - Summary of BoM solar radiation data for the analysis period Table Performance Results Table 13 - Sub Array Yield Factors (kwh/kwp) for Table Performance Results Table 15 - Sub Array Yield Factors (kwh/kwp) for Table Performance Results Table 17 - Sub Array Yield Factors (kwh/kwp) for Table 18 - Performance Results of the Overall System Table 19-System Performance Ratio Table 20 - Monthly AC Energy Efficiency Table 21 - PV Cell Results vii

8 1. Introduction Renewable resources accounted for % of generation of Australia s electricity in There were 322,000 solar power systems installed nationwide in 2012 and currently there are a total of over one million solar power systems installed in Australian homes[1]. Murdoch University has a number of renewable energy facilities which include the Renewable Energy Engineering Lab, Renewable Energy Outdoor Test Area and the Photovoltaic (PV) training Facility to name a few[2].the university is also producing 56 kw of solar power into the grid by the Library PV System. As there has been a rapid growth in PV technology and PV power systems some people want to know more about their systems and how they are performing. The ability to optimise the performance of PV systems is becoming more essential as the PV business becomes more competitive. The key focus of this thesis involves designing an affordable data monitoring system to be applied to a residential PV system, in order to review the performance of the system. Another key focus of this thesis is to complete a performance analysis of a PV system which involves system yield, performance ratio, and ac energy efficiency, as well as exploring what factors affects their parameters. 1

9 1.1. Project Objectives There were two significant objectives completed within this thesis. The first of these objectives was to design a data monitoring system (DMS) which had to be installed to a residential PV system. The DMS had to have the ability to determine the performance ratio and a.c energy efficiency of the system, at low cost, whilst being reasonably accurate and being independent of the inverter. The second main object was to complete a performance analysis on the Murdoch University Library Photovoltaic System (MULPVS). The analysis parameters of this PV system include the system and sub array yields, performance ratios, a.c energy efficiency, PV cell technology comparison and shading effects Scope of Work There are many tasks involved in this thesis project which allow the main objectives to be achieved. These are as follows: 1. Research data monitoring systems 2. Design a data monitoring system which meets the design requirements 3. Liaise with Sales Engineers to acquire quotations 4. Arrange equipment for data monitoring system 5. Sensor positioning investigation relating to the solar radiation sensor 6. Data retrieval of MULPVS and documentation of this process 7. Performance analysis of the MULPVS 2

10 1.3. Literature Review To gain an initial understanding of what a thesis entailed and what to include in this project, the reading of two theses was completed. These theses were by Stephen Rose and by Mael Riou titled Performance evaluation, simulation and design assessment of the 56kWp Murdoch University Library photovoltaic system [3]and Monitoring and data acquisition system for the photovoltaic training facility on the engineering and energy building [4]respectively. The basis of the performance analysis completed in this paper was a continuation from S. Rose s thesis. The performance analysis also refers to IEC Photovoltaic system performance monitoring Guidelines for measurement, data exchange and analysis [5] and also explores the ideas raised in the paper by David L. King More efficient methods for specifying and monitoring PV system performance [6]. The design of the data monitoring system also refers to the paper by D.L. King and the IEC Other literature which was used included SMA and Oneset websites exploring the different possible technologies to be used in the data monitoring system. 3

11 2. Background 2.1. Murdoch University Photovoltaic System As apart the Murdoch University s Environmental Sustainability Program 15% of electricity at the university was to be produced from renewable energy resources[7]. In order for this to become a possibility, the university installed a 56 kw photovoltaic (PV) system. The installation of the Murdoch University Library Photovoltaic System (MULPVS) was completed in two stages. The first stage instalment consisted of 192 x 135 W Kyocera poly- crystalline (poly- Si) PV panels. The size of this installation produced a peak rated power output of 26 kw. Included in the instalment were four SMA SMC 6000A inverters. The system was installed by Solar Unlimited and was completed in The second instalment occurred in 2009 and consisted of an addition of 171 x 175 W Sun Grid mono- crystalline PV panels. Again, SMA MC 6000A inverters were used and an additional 5 inverters were added to the system. This instalment produces a peak rated power output of 30 kw. The system was installed by Solar PV. The layout of this system is made up of 9 sub arrays. Sub arrays 1 to 4 are made up of two parallel strings with 24 panels in each string with a peak output of 6.48 kw, sub arrays 5 and 9 are made up of three parallel strings with 12 panels in each string with a peak output of 6.3 kw and sub arrays 6, 7 and 8 are made up of three parallel strings with 11 panels in each string with a peak output of kw. The following Figure 1shows a schematic diagram of the layout, displays the locations of each sub array. 4

12 5 Figure 1 - MULPVS Layout (Adapted from Stephen Rose thesis [3])

13 PV Modules The PV modules installed in the first stage were the Kyocera 135 W poly- crystalline panels (KD135GH-2P), 192 panels were installed and the specifications of this module are shown in Table 1. Table 1 - KD135GH-2P Specifications [8] Symbol Rating Unit Maximum Power Pmax 135 W Tolerance 5 % Maximum Power Voltage Vmpp 17.7 V Maximum Power Current Impp 7.63 A Open Circuit Voltage Voc 22.1 V Short Circuit Current Isc 8.37 A Maximum System Voltage 1000 V Conversion Efficiency 16 % The PV modules installed in the second stage were the Sun Grid 175 W mono- crystalline panel (SG-175M5), 171 panels were installed and the specifications of this module are shown in Table 2. Table 2- SG-175M5 Specification [9] Rating Unit Maximum Power 175 W Tolerance 5 % Maximum Power Voltage 35.2 V Maximum Power Current 4.97 A Open Circuit Voltage V Short Circuit Current 5.48 A Module Efficiency >13.7 % Solar Cell Efficiency >16.5 % 6

14 Inverter The inverter used in all nine sub arrays is the SMA Sunny mini Central 6000A inverter. The specifications for this inverter can be seen in the following Table 3. Table 3- SMA SMC 6000A Specifications [10] Rating Unit Maximum DC Input Power 6300 W Maximum DC Voltage 600 V PV Voltage rage, MPPT V Maximum Input Current 26 A Nominal AC Output Power 6000 W Maximum AC Output Power 6000 W Maximum Output Current 26 A Maximum Efficiency 96.1 % 2.2. Leeming Photovoltaic System The PV system which the data monitoring system (DMS) is applied to is located at Dr Martina Calais residence. For the purpose of this report this system is referred to as the Leeming PV System (LPVS). This system is a 1.1 kw system which includes 7 Sun Rise 190 W mono- crystalline panels along with a SMA SB1100 inverter. The image shown in Figure 2 is the LPVS. Figure 2- Leeming PV System 7

15 PV module As previously stated the PV module used in the system is the Sun Rise 190 mono- crystalline panel. The specifications of this panel can be seen in Table 4 below. Table 4 - Sun Rise 190W Specification[11] symbol Rating Unit Maximum Power Pm 190 W Tolerance 3 % Open Circuit Voltage Voc V Short Circuit Current Isc 5.70 A Maximum Power Vm V Voltage Maximum Power Im 5.28 A Current Module Efficiency % Solar Cell Efficiency % Inverter The inverter used in the LPVS is a SMA SB1100 inverter, and the specifications of this inverter can be seen in Table 5. Table 5 - SMA SB1100 Specifications[12] Rating Unit Maximum DC Input Power 1210 W Maximum DC Voltage 400 V PV Voltage rage, MPPT V Maximum Input Current 10 A Nominal AC Output Power 1000 W Maximum AC Output Power 1100 W Maximum Output Current 5.6 A Maximum Efficiency 93.0 % 8

16 3. Design of Data Monitoring and Data Acquisition System 3.1. Requirement Criteria A major requirement of this project was to implement the design of a Data Monitoring System (DMS). This DMS was to be a basic system which would monitor the LPVS. The purpose of the monitoring system was to carry out a performance assessment of the LPVS. In the paper More efficient methods for specifying and monitoring PV system performance by David L. King, it suggests that a.c energy efficiency is an advantageous way to analyse a PV system [6]. This was done because a basic monitoring system would be needed, which would include solar radiation and a.c energy output sensors. The DMS was to include a data logger, solar radiation sensor and an energy (kwh) sensor at minimum. The solar radiation and energy measured are the only parameters necessary for the calculation of the system s a.c energy efficiency. Other requirements were that the DMS was to be a low cost system and it was suggested that international standard IEC Photovoltaic system performance monitoring Guidelines for measurement, data exchange and analysis should be referred to during the design[5] Selection Criteria The sensors for the DMS were selected on the basis of a combination of cost and accuracy. On this note, a number of components were looked into and tables were produced which outline the cost and accuracy of the components. These tables were used to help select the most appropriate option for this system. 9

17 3.3. Design Process and Methodology The design process was extensive and involved research and communication with suppliers and supervisor Martina Calais. The first step for this process was to explore a range of different possible technologies to select from. From the research it was found there were a few possibilities for the design development of the DMS. This includes SMA technology, Onset HOBO Data Loggers, Data Taker, Campbell Scientific Australia and Unidata. From the research it was quickly evident that some technologies would be expensive, so the two main equipment preferences were the SMA technology and Onset HOBO Data Loggers. After narrowing down the companies to design the system, feasible design options were developed to be analysed and compared against each other Selection for cost and accuracy comparison As previously stated the two main design options which were to be considered were the SMA technology or the HOBO data logger technology. The following sections explore the process for final selection SMA Technology Research into SMA technology was conducted and the components needed to be compatible with the SMA SB1100 inverter. There were a number of components which were possible options for the design which are shown in Table 6 and Table 7. 10

18 Table 6 - SMA Webbox Options 1 and 2 SMA Option Option 1 Option 2 Component Accuracy (%) SMA Sunny Sensorbox ±8 517 SMA- Sunny Webbox Bluetooth Inverter communication card Sunny Portal Not specified Not Specified Not Specified Cost ($) 660 $135 SMA Sunny Sensorbox ±8 517 SMA-Sunny Webbox + RS485 interface cable Inverter communication card Sunny Portal Not Specified Not Specified Not Specified Free website 682 $135 Free website Total Cost ($) Both designs shown in Table 6 include a SMA Sunny Sensorbox which is a component that can measure solar radiation, wind speed, ambient temperature and module temperature[13].they also include a Sunny Webbox which is a communication device, one of these devices communicates via Bluetooth and no cables are needed while the other communicates using cable connecting to the communication card[14, 15]. Both these devices communicate with the PV inverter and collect all the data which is then transmitted to the Sunny Portal. Sunny Portal is a website that stores and manages all the data and is accessed via the internet, via PC s or mobile phones. There are reporting functions within Sunny Portal which provide regular updates via [16]. It was found that the use of a Sunny Webbox was not the only way to communicate with the SMA SB1100 inverter. A SMA Bluetooth Piggy-Back card can be installed directly to the inverter and from this device it can be communicated via Bluetooth to the Sunny Explorer where all the data can be collected. Sunny Explorer is a free program which can be downloaded from the SMA website[17, 18]. 11

19 Table 7- SMA Option 3 SMA Option Component Accuracy (%) Cost ($) Total Cost ($) SMA Bluetooth Piggy-Back Not Specified Option Sunny Explore Not Specified Free website Onset HOBO Data Loggers Onset is a company that develops data monitoring equipment. This company produces a wide range of HOBO Data Loggers with different levels of capabilities to suit one s personal needs at an affordable price. A basic system design was put together and is shown in Table 8. This design was reasonably priced and the accuracies of all sensor devices were similar to the IEC specifications. Table 8 - Onset Basic System[13, 19, 20] Design Option Option 1 Components Data Logger Solar Radiation sensor Energy sensor Components Other Sensor Components Micro Station H Keyspan USB-to-serial adaptor Silicon Pyranometer Sensor Wattnode kwh transducer 240VAC Accuracy ±5 sec per week at 25 C Cost ($) Not Specified ±10 W/m 2 or ±5 % ±0.50% of reading Amp - Split-Core ±0.75% Current Transformer Pulse Input Adapter Not specified HOBOware Pro for Mac Not Specified or PC SA Freight - Interstate Not Specified Air Bag GST ($) Total Cost($including GST)

20 Final Selections After exploring these two possible options HOBO data logger technology was selected for the DMS. This was because the accuracy was better and the capabilities allowed for additional sensors to be added. The overall price for the basic system was $ which is only $105.13more than the cheapest SMA option 1. This selection took place in meetings with supervisor Martina Calais after discussions of both the advantages and disadvantages of the two technologies Design Development Once the HOBO data logger technology was chosen, further development was then carried out on the design and it was suggested that the cost of adding more sensors was feasible. The suggestion of adding more sensors was put up for discussion by supervisor Martina Calais during meetings. This lead to the development of the final design which required communication with suppliers engineers to confirm the design and a final quotation was requested which outlines all parts needed for the DMS. This can be seen in Appendix A Design Specification. The additional sensors to be added to the design include module temperature, a.c voltage and a.c current. These components are also recommended by the Australian Photovoltaic Association (APVA)[21]and IEC In order for these sensors to be applied to the design an upgrade in the data logger was needed. The Micro Station data logger which only had the capabilities for four sensors was omitted and the Energy logger was then selected as it had 15 channels. 13

21 3.4. Final DMS Design The final design is made up of the following five sensors; solar radiation, energy (kwh), module temperature, a.c voltage and a.c current. Table 9 below shows the cost and accuracy of the components used in the design. Figure 3 shows a diagram of how the design components will be connected. Table 9 - Onset Final System Components[13, 17, 19, 20, 22-24] Design Option Components Sensor Components Accuracy Data Logger Energy Logger H ±5 sec per week at 25 C Cost ($) Keyspan USB-to-serial adaptor Not Specified Total Cost ($ including GST) Option 2 Solar Radiation sensor Energy sensor Temperature Sensor AC Voltage and Current Sensors Silicon Pyranometer Sensor Wattnode kwh transducer 240VAC 15 Amp - Class 1.2 ACT Series Split-Core Current Transformer ±10 W/m2 or ±5 ±0.50% of reading ± Pulse Input Adapter Not specified Smart Temp Sensor ±0.2 C (from to 50 C) 5m Smart Sensor Extension Not Specified Cable Flex Smart TRMS Module Trans,0-300V,333mV (Voltage Sensor) Trans, Mini AC split, 10 amp 0.333vac CT (Current Sensor) ±0.3% of reading ±1% ±1% Other HOBOware Pro for Mac or PC Not Specified SA Freight - Interstate Air Bag Not Specified GST ($)

22 Figure 3- Diagram of DMS Design (For the purpose of observing the temperature sensor the component is place above the array which in actual fact would be under the solar panels.) Solar Radiation Sensor The selected solar radiation sensor is the Silicon Pyranometer Sensor and all the specification can be seen in Appendix A Design Specification. This component was selected as it had 5% accuracy and was reasonably priced. This component s calibration parameters are all stored inside the sensor and automatically communicate with the data logger without the need to extensively set up or program the device[13] Temperature Sensor The selected temperature sensor is the 12- Bit Temperature Smart Sensor and all the specification can be seen in Appendix A Design Specification. This component is designed to 15

23 work with the HOBO data loggers with the smart sensor plug-in, this allows for easy setup. This component has all the sensing components stored inside the sensor and automatically communicates with the data logger without any programming needed in the setup[17] AC Energy Sensor The selected energy sensor is comprised of three components a transducer, a current transformer and a pulse input adapter cable. The transducer and current transformer (CT) is connected together to measure energy generated. The CT is simply attached to the inverter active cable via the transducers in order to measure the energy (kwh). The capabilities of the transducer provide accurate measurement for energy metering [19]. Specification for the transducer and CT can be found in the tables in Appendix A Design Specification AC Voltage and Current Sensors These two sensors operate in connection to a module which connects to the data logger. This module supports a.c voltage and a.c current measurements[22]. An a.c potential transformer is used to measure the a.c voltage [20] and a current transformer is used to measure a.c current [23]. The specification of the a.c voltage and a.c current sensor can be found in Appendix A Design Specification HOBO H Energy Logger The selected data logger was the HOBO H Energy Logger. All the specifications for this system can be seen in Appendix A Design Specification. This logger was selected as it is a 15 channel system and has the ability to log several different types of sensors. The system is very functional as it is a plug and play system which makes it easy to set up, as the sensors insert to the slots and no complex programming is needed in order for this system to operate [24]. 16

24 3.5. Discussion of the DMS The DMS was initially designed to monitor only solar radiation and the energy produced by the system. These parameters are the only measurements required for the calculation of the a.c energy efficiency ( ), which determines the overall efficiency of the PV array. As previously stated, in the paper by David L. King, he suggested that the is an advantageous for specifying the performance of PV systems. There are several advantages of determining the a.c energy efficiency including the following: - The total system performance which includes all components and derating factors are represented by the. - Uncertainty of performance metrics are avoided given that power ratings are not required. - The is straight forwardly interpreted by the PV specialist and financial community [6]. A requirement of the DMS design was that the system cost had to be relatively low, as the system is for a residential 1.1 kw PV system therefore, cheaper data monitoring system technologies were proposed for selection. Another factor for the selection was the accuracy of each sensor. These accuracies were determined by referring to the IEC standards which are shown Table 10. Table 10 - IEC Measurement Accuracies [5] Parameter Accuracy Solar Radiation 5 % Module Temperature 1 K (1 C) Voltage 1 % Current 1 % Power 2 % 17

25 The Onset HOBO data logger technology was selected to be used to design the DMS because of the reasonable cost, the accuracy of all measurement components which were all within close range of the IEC standard and the possibility to add more sensors if desired. The SMA technology was similarly priced but the accuracy was of inferior quality and the sensor capabilities were limited. The chosen technology had the capabilities to add addition sensors, so more sensors were added. Ultimately the HOBO data loggers were far superior in quality in term of accuracy and cost. The total system cost for the DMS came to $ (including GST) which includes solar radiation, energy (kwh), module temperature, a.c voltage and a.c current sensors. An investigation of where to mount the solar radiation sensor was also carried out. This was done by using a solar pathfinder to determine the location on the array which was least exposed to shading. Pictures of this investigation can be seen in Appendix B Solar Pathfinder Images. 18

26 4. Performance Analysis A major task of this project is to analyse the performance of the MULPVS. As the MULPVS has had a performance analysis carried out previously for the time period between August 2010 and May 2011 by Stephen Rose[3], there will be a continuation of this analysis for the time period between June 2011 and August Performance Analysis Method The method for calculating the performance ratio and a.c energy efficiency follows the method David L. King uses in his paper which was previously mentioned. This process follows the IEC international standard. This method involves a number of performance indices to complete a performance analysis. The main derived parameters are as follows: - Final Yield - Reference Yield - Performance Ratio - System AC Energy Efficiency Before the performance indices are calculated, the calculations of monthly yield were obtained. Prior to this being done the sunny Webbox data files from the MULPVS have to be converted to useable data. This process is outlined in Appendix C Sunny SensorBox Data Retrieval. Monthly ac energy produced are calculated by the following equation: 19

27 The monthly yield is calculated for each sub-array (inverter).total system yields are obtained by adding all the sub arrays outputs. The summary of these results can be found in Appendix D Performance Results. From this table an analysis was carried out exploring the best and worst performing months, best and worst performing sub arrays, average output and total system yield Final Yield The final yield ( ) involves dividing the a.c energy produced ( ) by the system, by the rated power output of the system ( ). is the measure of a.c energy (kwh) produced by the PV system over time and being measured. is the array s rated output (kwp) of the PV system Reference Yield The reference yield ( ) involves dividing the cumulative plane of array irradiance ( thereference irradiance level ( ). is the measure of cumulative plane of array ) by irradiance (kwh/m 2 ), and is the global reference irradiance level which is 1 kw/m 2 as suggested by IEC 61724[5] Performance Ratio The performance ratio ( ) involves dividing the final yield ( ) by the reference yield ( ). The is the system s and sub array s overall conversion efficiency, of the energy received 20

28 to the energy exported to grid[3]. The accounts for the losses associated with the system which include, temperature derating, dirt derating, cable losses, inverter efficiency, shading, tolerances and degradation System AC Energy Efficiency The system a.c energy efficiency ( ) involves cumulative plane of array irradiance ( ), the array area (A) in m 2 and the energy produced ( ) by the system. The in general terms is the ratio of energy produced by the system to the energy supplied to system[6]. 21

29 5. Performance Analysis Results of MULPVS 5.1. Solar Radiation Method for Solar Radiation Data The solar radiation data used for the performance analysis was initially obtained from the Bureau of Meteorology (BoM) climate data [18]. The BoM has a Murdoch station (station number ) which records the solar exposure for the latitude and longitude coordinates of S and E respectively. The accuracy of this data is stated to be 7% for clear sky days and up to 20% for cloudy days[25].the BoM climate data website produces the monthly mean daily solar radiation for each month from 1990 to present, for solar exposure on a horizontal plane [26]. The PR results show abnormally high PR reaching above 0.90, so it was assumed that there were some errors with these calculations and the BoM solar radiation data was omitted. Instead of using the BoM data, the solar radiation data produced by the Murdoch Met Station, which is located at the latitude and longitude of S and E respectively, was used. This data produced better results, as the radiation data showed higher readings than that of the BoM. The low solar radiation data from the BoM and the high system yield caused the performance ratio to be relatively high. The Met station had higher solar radiation data measurements, which reduced the performance ratio values as there was more available solar energy to be converted into power. The horizontal data used was converted to plane of array data using an excel spreadsheet which was provided by Dr Trevor Pryor of Murdoch University[27]. This spreadsheet was produced by using the equations and theory from the text Solar Engineering of Thermal Processes by J.A. Duffie and W.A. Beckman[26]. Some of the equations to convert the horizontal to in plane of array solar radiation used the sunset hour angle, extra-terrestrial 22

30 radiation, clearness index, beam radiation and diffuse radiation, the equations are then applied using the KT method outline in the text Solar Radiation Data Trend The time period for the performance analysis of the MULPVS is from June 2011 to August Below, Figure 4is a graph showing the plane of array monthly mean daily solar radiation for Murdoch for 2011, 2012 and 2013 during the analysis period. These results were calculated from the Met Stations horizontal solar radiation measurements using the method explained previously. For the 2011 analysis period it can be seen that the months of July, October, November and December produce results below average solar radiation levels, and June, August and September produce results above average solar radiation levels. For 2012 it can be seen that the months of January, June and December produce results below average solar radiation levels and February, March, April, July, August, September, October and November produce results above average solar radiation levels. For the 2013 analysis period it can be seen that January to August all produce above average solar radiation levels. 23

31 Daily Average Radiation (kwh/m^2) 9 Incident Radiation on Plane of Array for Murdoch (Slope: 23 ) Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Month BOM Yearly Average Figure 4 - Incident Radiation on Plane of Array for Murdoch (Slope: 23 ) Table 11 is a list of the monthly average solar radiation on the plane of array, this table also includes the monthly average for all year which is from 1990 to present as well as the difference between each year and the monthly average for all years. This can suggest that the expected output for the system will vary with the change in solar radiation, so when there is greater solar radiation levels the system output will be greater and when there is less solar radiation levels there will be less system output generated. This is evident in Figure 5. 24

32 Solar Radiation (kwh/m^2) Energy Generated (MWh) Table 11 - Summary of BoM solar radiation data for the analysis period 2011 Monthly Average (kwh/m 2 ) 2012 Monthly Average (kwh/m 2 ) 2013 Monthly Average (kwh/m 2 ) Monthly Average (From 1990 to Present) (kwh/m 2 ) 2011 Difference from Month average of all years (kwh/m 2 ) 2012 Difference from Month average of all years (kwh/m 2 ) 2013 Difference from Month average of all years (kwh/m 2 ) Month Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Monthly Solar Radiation and Monthly System Outputover Time Month of Year Monthly solar radiation Monthly System Output Figure 5 - Monthly Solar Radiation and Monthly System Output over Time 25

33 Solar Radiation (kwh/m^2) AC Energy Efficency (%) Figure 6 shows a graph comparing monthly solar radiation and system a.c energy efficiency over time, this graph shows that the variation of the solar radiation per month has little effect on the a.c energy efficiency Monthly Solar Radiation and System AC Energy Efficiency over Time 11.50% % % 10.00% 9.50% 9.00% 8.50% 8.00% Monthly solar radiation Month of Year System AC Energy Efficiency Figure 6 -Monthly Solar Radiation vs. System AC Energy Efficiency over Time 5.2. System Output The system output analysis time period is again from June 2011 to August To have an in depth examination there will be four sections analysed. These are 2011 time period from June until December, all months in 2012, 2013 from January to August and the total system output for the entire time period. 26

34 Energy Generated (kwh) June to December of 2011 System Outputs As previously stated the analysis period for 2011 is between June and December. The system produced a total of MWh for these seven months. The individual sub array (SA) energy generation is shown on Figure 7. This graph shows the energy generated for each month in kwh. Each sub array corresponds to a different inverter and so sub array 1 corresponds to inverter 1, sub array 2 corresponds to inverter 2 and so on Monthly Sub-Array Energy Production Month Sub Array 1 Sub Array 2 Sub Array 3 Sub Array 4 Sub Array 5 Sub Array 6 Sub Array 7 Sub Array 8 Sub Array 9 Figure Monthly Sub-Array Energy Production Production peaked in December with the system generating a sum of 9.52 MWh for this month. It also was found that production was at its lowest during June having the system only generate 4.76 MWh, which is approximately half of the energy produced in December. The sub array which had the best production within this period of time was found to be SA 2 generating a total of 6.23 MWh, and the worst producing sub arrays were SA 7 and SA 8, both generating 5.37 MWh. These performance results and others are shown on the below Table

35 Table Performance Results Output Sub Array Month Unit Max Monthly Inverter Output 1.13 Sub Array 2 December MWh Min Monthly Inverter Output 0.46 Sub Array 8 June MWh Max Monthly Output 9.52 Not Applicable December MWh Min Monthly Output 4.76 Not Applicable June MWh Sub Array with the Most Output 6.23 Sub Array 2 Not Applicable MWh Sub Array with the Least Output 5.37 Sub Array 8& Sub Array 7 Not Applicable MWh Average Total Sub Array Output 5.84 Not Applicable Not Applicable MWh Average Total Monthly Output 7.51 Not Applicable Not Applicable MWh Overall System Output Not Applicable Not Applicable MWh The following Table 13 Shows the sub array yield factor in kwh/kwp. It is found in this table that the best yield factor month is December producing a monthly average of kwh/kwp, and the worst yield factor month is June producing a monthly average of kwh/kwp. It was also found that the overall average sub array monthly yield factor was kwh/kwp. Table 13 - Sub Array Yield Factors (kwh/kwp) for 2011 Month SA 1 SA 2 SA 3 SA 4 SA 5 SA 6 SA 7 SA 8 SA 9 System Jun Jul Aug Sep Oct Nov Dec Average

36 Energy Generated (kwh) January to December 2012 System Outputs The analysis period for 2012 is between January and December. The system output for 2012 was MWh. During the months February, March, April, May and June the inverter of SA 3 was offline. This affected the system output for these months resulting in lower outputs. Shown in Figure 8 is a graph of the monthly sub array power production for 2012 displaying the variation of the energy generation over the year. It is interesting to note that if SA 3 was online that the energy generated in March could have been similar to that of January if not more Monthly Sub-Array Energy Production Sub Array 1 Sub Array 2 Sub Array 3 Sub Array 4 Sub Array 5 Sub Array 6 Sub Array 7 Sub Array 8 Sub Array 9 Month Figure Monthly Sub Array Energy Production Production peaked in December with the system generating a sum of 9.98 MWh for this month and it also was found that production was at its lowest during June having the system only generate 3.43 MWh. The sub array which had the best production within the period of time was found to be SA 2 generating a total of 11.46MWh and the worst producing sub array was SA 3 generating 6.97 MWh. SA 3 produced a significantly lower output for the previously stated reason, namely that this sub array was offline during the months of February to June. These results and others are shown in Table 14 below. 29

37 Table Performance Results Output Sub Array Month Unit Max Monthly Inverter Output 1.20 Sub Array 2 January MWh Min Monthly Inverter Output 0.37 Sub Array 8 June MWh Max Monthly Output 9.98 Not Applicable December MWh Min Monthly Output 3.43 Not Applicable June MWh Sub Array with the Most Output Sub Array 2 Not Applicable MWh Sub Array with the Least Output 6.97 Sub Array 3 Not Applicable MWh Average Total Sub Array Output Not Applicable Not Applicable MWh Average Total Monthly Output 7.73 Not Applicable Not Applicable MWh Overall System Output Not Applicable Not Applicable MWh The following Table 15 shows the sub array yield factor in kwh/kwp. It is evident in this table that the high yield factor months are January and December producing a monthly average of kwh/kwp and kwh/kwp, respectively. The lowest yield factor month is significantly lower with June producing a monthly average of kwh/kwp. It was found that the overall average sub array monthly yield factor was kwh/kwp. 30 Table 15 - Sub Array Yield Factors (kwh/kwp) for 2012 Month SA 1 SA 2 SA 3 SA 4 SA 5 SA 6 SA 7 SA 8 SA 9 Average Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Average

38 Energy Generated (kwh) January to August 2013 System Outputs The analysis period for this section is from January to August of The system produced a total of MWh for these seven months. The monthly sub array power production is shown in Figure Monthly Sub-Array Power Production January February March April May June July August Month Sub Array 1 Sub Array 2 Sub Array 3 Sub Array 4 Sub Array 5 Sub Array 6 Sub Array 7 Sub Array 8 Figure Monthly Sub Array Energy Production Production has so far peaked in January with the system generating a sum of MWh and it also was found that production was at its lowest during July generating a sum of 3.91 MWh. The sub array which had the best production within the period of time was found tobe SA 3 generating a total of 6.84 MWh and the worst producing sub array was SA 8 generating 5.75 MWh. These results and others are shown in Table 16 below. 31

39 Table Performance Results Output Sub Array Month Unit Max Monthly Inverter Output 1.21 SA 2 January MWh Min Monthly Inverter Output 0.49 SA 8 June MWh Max Monthly Output Not Applicable January MWh Min Monthly Output 5.26 Not Applicable July MWh Sub Array with the Most Output 6.84 SA 3 Not Applicable MWh Sub Array with the Least Output 5.75 SA 8 Not Applicable MWh Average Total Sub Array Output 6.37 Not Applicable Not Applicable MWh Average Total Monthly Output 7.17 Not Applicable Not Applicable MWh Overall System Output Not Applicable Not Applicable MWh The following Table 15 shows the sub array yield factor in kwh/kwp for this time period. The best yield factor month was January producing a monthly average of kwh/kwp, and the worst yield factor month was July producing a monthly average of kwh/kwp. It was also found that the overall average sub array monthly yield factor for this time period was kwh/kwp. Table 17 - Sub Array Yield Factors (kwh/kwp) for 2013 Month SA 1 SA 2 SA 3 SA 4 SA 5 SA 6 SA 7 SA 8 SA 9 System Jan Feb Mar Apr May Jun Jul Aug Average

40 Energy Generated (MWh) Overall System Output The time period for this performance analysis is from June 2011 to August The overall system output for this 27 month time period is MWh. Shown in Figure 10 is a graph of the monthly system output for the analysis period. This shows the variation of the energy generated from month to month. 12 System Monthly Output Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Month Figure 10 - Overall System Output The production of energy for the overall time period peaked during January 2013 with the system generating a sum of MWh in terms of monthly output. The system saw its lowest production in June 2012 generating a low3.43mwh. It was found that the sub array which had the best production was SA 2 generating a total of 24.48MWh which is % of total production and the worst producing sub array was SA 3 generating a total of MWh which is 9.85 % of total production. These results and others are shown on the below Table

41 Table 18 - Performance Results of the Overall System Output Sub Array Month Unit Max Monthly Inverter Output 1.21 Sub Array 2 January 2013 MWh Min Monthly Inverter Output 0.37 Sub Array 8 June 2012 MWh Max Monthly Output Not Applicable January 2013 MWh Min Monthly Output 3.43 Not Applicable June 2012 MWh Sub Array with the Most Output Sub Array 2 Not Applicable MWh Sub Array with the Least Output Sub Array 3 Not Applicable MWh Average Total Sub Array Output Not Applicable Not Applicable MWh Average Total Monthly Output 7.51 Not Applicable Not Applicable MWh Overall System Output Not Applicable Not Applicable MWh The following Figure 11 shows the graph of the monthly average yield factor in kwh/kwp over the 27 month period. It can be seen in this graph that the best yield factor month was January in 2013 producing a monthly average of kwh/kwp, and the worst yield factor month was June in 2012 producing a monthly average of kwh/kwp. It was also found that the overall average sub array monthly average yield factor for this 27 month period to be kwh/kwp. In Appendix D Performance Results a table of monthly sub array and total system yield factor can be seen. 34

42 Energy Generated (MWh) Monthly Average Yield Factor per Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Month Figure 11 - Monthly Average Yield Factor per Year (kwh/kwp) 35

43 System Output Discussion Factors that can affect the system output are shading, dirt build up, temperature and solar radiation levels. The factors of dirt build up and temperatures are assumptions as investigation of these aspects were not carried out. It can be assumed that the build up of dirt has affected the PV system during the months of During the months of April, May, July and August there were greater levels of solar radiation but the system outputs for these months were lower than in the same month of 2012, this can also be caused by shading from the trees which have grown from year to year. It was found that the average daily temperature during January for 2012 and 2013 was 33.5 C and 31.7 C respectively, while the system outputs were 9.94 MWh and MWh respectively. This suggests that high temperatures can affect the power output of the panels. When looking at the variation of solar radiation this directly affects the power output of the PV system. The best sub array in terms of energy generation was found to be SA 2. It can be assumed that this was due to the location of the array as it is nearly in the middle of the system being exposed to shading the least. The peak output of the array is 6.48 kw. It was found that the worst sub array was SA 3.This is because during the months from February to June of 2012 the array was offline. It can be assumed that if SA 3 was online during the whole 27 month period the worst array would then be SA 7 as it is affected by shading and the array has a peak output of kw. The best producing month of the system was found to be January 2013.This is a result of high levels of solar radiation and the lower temperatures present. The worst producing month was June of 2012 and this is a result of low solar radiation levels, shading on the system as well as the SA 3 being offline during this month. 36

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