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Thermal Characteristics of Microinverters on Dual-Axis Trackers

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Thermal Characteristics of Microinverters on Dual-Axis Trackers THERMAL CHARACTERISTICS OF MICROINVERTERS ON DUAL-AXIS TRACKERS by MOHAMMAD AKRAM HOSSAIN Submitted in partial fulfillment of the requirements For the degree of Master of Science Department of Mechanical and Aerospace Engineering CASE WESTERN RESERVE UNIVERSITY May, 2014 Thermal Characteristics of Microinverters on Dual-axis Trackers Case Western Reserve University Case School of Graduate Studies We hereby approve the thesis1 of MOHAMMAD AKRAM HOSSAIN for the degree of Master of Science Dr. Alexis Abramson Committee Chair, Adviser 01/30/2014 Department of Mechanical and Aerospace Engineering Dr. Roger French Committee Member, Co-Adviser 01/30/2014 Department of Materials Science and Engineering Dr. Joseph Prahl Committee Member 01/30/2014 Department of Mechanical and Aerospace Engineering Dr. Yifan Xu Committee Member 01/30/2014 Department of Epidemiology and Biostatistics 1We certify that written approval has been obtained for any proprietary material contained therein. Thesis defense date: 01/30/2014 Dedicated to science and the pursuit of progress. Thermal Characteristics of Microinverters on Dual-axis Trackers Abstract by MOHAMMAD AKRAM HOSSAIN 0.1 Abstract The thermal characteristics of microinverters on dual-axis trackers operating under real-world conditions were analyzed using a statistical analytical approach. 24 microin- verters connected to 8 different brands of photovoltaic (PV) modules were analyzed from July through October 2013 at the Solar Durability and Lifetime Extension (SDLE) SunFarm at Case Western Reserve University (latitude 41.50, longitude -81.640). Ex- ploratory data analysis shows that the microinverters’ temperature is strongly correlated with ambient temperature and PV module temperature, and moderately correlated with irradiance and AC power. Ambient temperature is the influencing factor under condi- tions of low irradiance in morning hours, when the irradiance is below 60 W/m2. Noon- time data analysis reveals that the microinverters thermal behavior is more strongly in- fluenced by PV module temperature than AC power. Using a Euclidean distance measur- ing principle and average linkage criteria, a hierarchical clustering technique was also applied to noontime microinverter temperature data to group the similarly behaved mi- croinverters. Microinverter temperature clustering shows that the clustering groups are more strongly influenced by PV module temperature than AC power. A linear regression iv model was developed to predict the temperature of the microinverters connected to dif- ferent brands PV modules. The predictive model is a function of ambient temperature, PV module temperature, irradiance, AC power data, and the interaction between power, irradiance and module temperature. The difference between actual microinverter and predicted microinverter temperature lies between 0.40C to 1.60C at a 95% confidence interval. v Acknowledgements I would like to express my sincere gratitude to my co-advisors Dr. Alexis Abramson and Dr. Roger French for their continuous support, patience, motivation and guidance for this project. I also would like to thank the rest of my thesis committee: Dr. Joseph Prahl and Dr. Yifan Xu for their encouragement and insightful comments about this project. Thanks to Underwriters Laboratories (UL) for providing financial support for this project. I would like to especially thank to Dr. Timothy Peshek and Dr. Laura Bruckman for their insights, valuable comments and opinions while working on this project. Thanks to all the members in Solar Durability and Lifetime Extension Center for their diligence, patience and resourcefulness. I owe special thanks to Yang Hu and Zachary Baierl for assisting me a lot in building the test setup. Assistance and technical support from researchers from the Medical Informatics Di- vision of EECS, especially Yashwanth Reddy Gunapati and Tarun Jian were extremely valuable in completing this work. vi Table of Contents Abstract iv Acknowledgements vi List of Tables x List of Figures xii Chapter 1. Introduction1 World Energy Scenario2 Evolution of PV cell and module5 PV System 8 Thermal Model for PV System8 Inverter system 10 Microinverter History and Evolution 12 Microinverter Topology 13 Critical to Lifetime Performance (CLP) Components 14 Power Loss inside Microinverter 16 Reliability of Microinverters 17 Lifetime and Degradation Science 21 Chapter 2. Experimental Setup 24 SDLE SunFarm 24 Metrology Platform at SDLE SunFarm 27 Data Acquisition System 29 Experimental Setup Overview 30 vii Chapter 3. Analysis Methods 34 Data validation 34 Exploratory Data Analysis 36 Correlation Coefficient 36 Local Solar Time 37 Normalized Temperature 37 t-Test Statistics 37 Cluster Analysis 38 K-means clustering 38 Linear Regression Model 39 R software 40 Chapter 4. Results and Analysis 41 Exploratory Data Analysis (EDA) 41 Morning Data Analysis 45 Noon Data Analysis 49 Cluster Analysis 62 Linear Regression Model 66 Chapter 5. Discussion 71 Influence of Irradiance on PV Module and Microinverter Temperature 71 Influence of AC Power Output and PV Module Temperature on Microinverter Temperature 72 5.4 Influence of Ambient Conditions on Microinverter Temperature 74 Clustering Analysis 76 viii Linear Regression Model 78 Chapter 6. Conclusions 80 Chapter 7. Suggested Future Research 82 Appendix A. Preparation of this document 84 Appendix B. Thermal performances at different power range 85 Appendix. Complete References 90 ix List of Tables 1.1 List of CLP of components with stressors, common failure modes and effects on the microinverter 15 4.1 Correlation coefficient for different variables for the Q.t12 systems 43 4.2 Correlation coefficient for different variables for the Q.t12 systems in the morning time from July through October, 2013 45 4.3 Comparison of PV module temperature, and microinverter temperature with ambient temperature at different irradiance zone for the Q.t12 system in the morning from July through October, 2013 47 4.4 Variation of normalized module and microinverter temperature at different irradiance zone for the Q.t12 system in the morning from July through October, 2013 48 4.5 Correlation coefficient for different variables for the Q.t12 systems at noontime from July 1 through October 30, 2013. 49 4.6 Thermal performances of the P.t.12 systems at different power range from July 1 through October 30, 2013. 51 4.7 Thermal performances of the T.t.14 systems at different power range from July 1 through October 30, 2013. 52 4.8 Thermal performances of the L.t.6 systems at different power range 53 4.9 The average power and temperature difference between the L.t.6 at noon time 53 x 4.10 The rise in temperature in the site 12 PV modules and microinverters during noon time 54 4.11 The coefficient values of different variables for the predictive model (Equation 4.1) 67 B.1 Thermal performances of O.t.12 modules at different power range 85 B.2 Thermal performances of R.t.14 modules at different power range 86 B.3 Thermal performances of S.t.14 modules at different power range 87 B.4 Thermal performances of K.t.6 modules at different power range 88 B.5 Thermal performances of Q.t.12 modules at different power range 89 xi List of Figures 1.1 World energy consumption in year 20117 3 1.2 Annual investment in different renewable energy sectors across the world between 2001-20124 1.3 Different type of inverter systems: a) Central /string inverter and b) microinverter assembly9 1.4 Different type of inverter systems: a) Central /string inverter and b) microinverter assembly 11 1.5 Circuit diagram of a microinverter 14 1.6 Unscheduled maintenance cost for PV system operation 18 1.7 Failure count for components of a PV system 19 2.1 Top figure: SDLE SunFarm layout, bottom figure: dual axis tracker with PV modules and sample trays 25 2.2 a) Thermocouple on the backsheet of the microinverter, b) data cabinet 28 2.3 Baseline DC output power of PV modules measure by SPIRE 4600 solar simulator 30 2.4 Solar irradiance from July 1 through October 30, 2013 32 2.5 AC Power output data for O.t12 systems from July 1 through October 30, 2013 33 3.1 Validation of thermocouple recorded temperature data 35 xii 4.1 Scatter pairs plots for the Q.t12 systems from July 1 through October 30, 2013. 42 4.2 Ambient Temperature vs. microinverter backsheet TC temperature for the Q.t12 systems from July through October 2013 43 4.3 Irradiance vs. AC Power output for the Q.t12 systems from July through October 2013 44 4.4 Variation in AC power output of microinverters in the Q.t12 systems for different ambient temperature range 45 4.5 Scatter pairs plots for the Q.t12 systems in the morning time from July through October, 2013 46 4.6 Irradiance vs normalized module backsheet and microinverter backsheet TC temperature in the morning for the Q.t12 systems 48 4.7 Scatter pairs plots for the Q.t12 systems at noontime from July 1 through October 30, 2013. 50 4.8 Normalized module backsheet, microinverter backsheet and microinverter internal temperature for the T.t14.1 system 54 4.9 Variation of normalized module backsheet temperature for different modules at different power range 56 4.10 Variation of normalized microinverter backsheet temperature for different modules at different power range 57 4.11 Variation of normalized module backsheet temperature for power range 190-210 W 58 xiii 4.12 Variation of normalized microinverter backsheet temperature for power range 190-210 W 59 4.13 Variation of normalized module backsheet temperature for power range above 210 W 60 4.14 Variation of normalized microinverter backsheet temperature for power range above 210 W 61 4.15 Hierarchical cluster analysis of time-series AC Power data at noon from July 1 through October 30, 2013. 62 4.16 Hierarchical cluster analysis of normalized PV module backsheet temperature time-series data at noon from July 1 through October 30, 2013.
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