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Microinverter Thermal Performance in the Real-World: Measurements and Modeling.

Hossain MA, Xu Y, Peshek TJ, Ji L, Abramson AR, French RH - PLoS ONE (2015)

Bottom Line: The importance of the covariates are rank ordered.The model allows for prediction of internal temperature for the Enphase M215 given similar climatic condition and can be expanded to predict microinverter temperature in fixed-rack and roof-top PV systems.This study is foundational in that similar models built on later stage data in the life of a device could reveal potential influencing factors in performance degradation.

View Article: PubMed Central - PubMed

Affiliation: Department of Mechanical and Aerospace Engineering, Case Western Reserve University, Cleveland, Ohio, United States of America; Solar Durability and Lifetime Extension (SDLE) Center, Case Western Reserve University, Cleveland, Ohio, United States of America.

ABSTRACT
Real-world performance, durability and reliability of microinverters are critical concerns for microinverter-equipped photovoltaic systems. We conducted a data-driven study of the thermal performance of 24 new microinverters (Enphase M215) connected to 8 different brands of PV modules on dual-axis trackers at the Solar Durability and Lifetime Extension (SDLE) SunFarm at Case Western Reserve University, based on minute by minute power and thermal data from the microinverters and PV modules along with insolation and environmental data from July through October 2013. The analysis shows the strengths of the associations of microinverter temperature with ambient temperature, PV module temperature, irradiance and AC power of the PV systems. The importance of the covariates are rank ordered. A multiple regression model was developed and tested based on stable solar noon-time data, which gives both an overall function that predicts the temperature of microinverters under typical local conditions, and coefficients adjustments reecting refined prediction of the microinverter temperature connected to the 8 brands of PV modules in the study. The model allows for prediction of internal temperature for the Enphase M215 given similar climatic condition and can be expanded to predict microinverter temperature in fixed-rack and roof-top PV systems. This study is foundational in that similar models built on later stage data in the life of a device could reveal potential influencing factors in performance degradation.

No MeSH data available.


Related in: MedlinePlus

Pairwise scatter plot matrix, histogram and correlation coefficients of all related variables for the microinverters connected with different brands of PV module.Pairwise scatter plots are in lower triangle boxes, histograms are in the diagonal boxes, and upper triangle boxes give the correlation coefficients between variables. Brand, Ambient. T, Wind speed, Module. T, Power and Micro. T denote PV module brand, ambient temperature, 5 point moving average wind speed, PV module temperature, AC power output and microinverter temperature respectively.
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pone.0131279.g002: Pairwise scatter plot matrix, histogram and correlation coefficients of all related variables for the microinverters connected with different brands of PV module.Pairwise scatter plots are in lower triangle boxes, histograms are in the diagonal boxes, and upper triangle boxes give the correlation coefficients between variables. Brand, Ambient. T, Wind speed, Module. T, Power and Micro. T denote PV module brand, ambient temperature, 5 point moving average wind speed, PV module temperature, AC power output and microinverter temperature respectively.

Mentions: Fig 2 shows the pairwise scatter plots (lower half), histograms and corresponding correlation coefficients (upper half) among irradiance, wind speed, ambient temperature, module temperature, AC power and microinverter temperature, for the cleaned data set [47, 48]. The ambient temperature and PV module temperature are strongly correlated with the microinverter temperature. Additionally, AC power, and irradiance are also moderately correlated with the microinverter temperature. However, the wind speed shows little correlation with any other variables measured in this study.


Microinverter Thermal Performance in the Real-World: Measurements and Modeling.

Hossain MA, Xu Y, Peshek TJ, Ji L, Abramson AR, French RH - PLoS ONE (2015)

Pairwise scatter plot matrix, histogram and correlation coefficients of all related variables for the microinverters connected with different brands of PV module.Pairwise scatter plots are in lower triangle boxes, histograms are in the diagonal boxes, and upper triangle boxes give the correlation coefficients between variables. Brand, Ambient. T, Wind speed, Module. T, Power and Micro. T denote PV module brand, ambient temperature, 5 point moving average wind speed, PV module temperature, AC power output and microinverter temperature respectively.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4493157&req=5

pone.0131279.g002: Pairwise scatter plot matrix, histogram and correlation coefficients of all related variables for the microinverters connected with different brands of PV module.Pairwise scatter plots are in lower triangle boxes, histograms are in the diagonal boxes, and upper triangle boxes give the correlation coefficients between variables. Brand, Ambient. T, Wind speed, Module. T, Power and Micro. T denote PV module brand, ambient temperature, 5 point moving average wind speed, PV module temperature, AC power output and microinverter temperature respectively.
Mentions: Fig 2 shows the pairwise scatter plots (lower half), histograms and corresponding correlation coefficients (upper half) among irradiance, wind speed, ambient temperature, module temperature, AC power and microinverter temperature, for the cleaned data set [47, 48]. The ambient temperature and PV module temperature are strongly correlated with the microinverter temperature. Additionally, AC power, and irradiance are also moderately correlated with the microinverter temperature. However, the wind speed shows little correlation with any other variables measured in this study.

Bottom Line: The importance of the covariates are rank ordered.The model allows for prediction of internal temperature for the Enphase M215 given similar climatic condition and can be expanded to predict microinverter temperature in fixed-rack and roof-top PV systems.This study is foundational in that similar models built on later stage data in the life of a device could reveal potential influencing factors in performance degradation.

View Article: PubMed Central - PubMed

Affiliation: Department of Mechanical and Aerospace Engineering, Case Western Reserve University, Cleveland, Ohio, United States of America; Solar Durability and Lifetime Extension (SDLE) Center, Case Western Reserve University, Cleveland, Ohio, United States of America.

ABSTRACT
Real-world performance, durability and reliability of microinverters are critical concerns for microinverter-equipped photovoltaic systems. We conducted a data-driven study of the thermal performance of 24 new microinverters (Enphase M215) connected to 8 different brands of PV modules on dual-axis trackers at the Solar Durability and Lifetime Extension (SDLE) SunFarm at Case Western Reserve University, based on minute by minute power and thermal data from the microinverters and PV modules along with insolation and environmental data from July through October 2013. The analysis shows the strengths of the associations of microinverter temperature with ambient temperature, PV module temperature, irradiance and AC power of the PV systems. The importance of the covariates are rank ordered. A multiple regression model was developed and tested based on stable solar noon-time data, which gives both an overall function that predicts the temperature of microinverters under typical local conditions, and coefficients adjustments reecting refined prediction of the microinverter temperature connected to the 8 brands of PV modules in the study. The model allows for prediction of internal temperature for the Enphase M215 given similar climatic condition and can be expanded to predict microinverter temperature in fixed-rack and roof-top PV systems. This study is foundational in that similar models built on later stage data in the life of a device could reveal potential influencing factors in performance degradation.

No MeSH data available.


Related in: MedlinePlus