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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

Variation in microinverter temperature and PV module temperature with irradiance in (a) the morning, and (b) noon time.Variation in microinverter temperature and PV module temperature with irradiance for Q.t12 PV microinverter and PV modules in the morning and noon time.
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pone.0131279.g004: Variation in microinverter temperature and PV module temperature with irradiance in (a) the morning, and (b) noon time.Variation in microinverter temperature and PV module temperature with irradiance for Q.t12 PV microinverter and PV modules in the morning and noon time.

Mentions: According to the distinct thermal characteristics of microinverters in different time ranges, we segregate two subsample data sets, dubbed “morning” and “noontime”, to isolate and observe the thermal attributes under relative low and high irradiance conditions. Morning time is defined as local solar time (LST) [49] from 05:00 to 06:30, and the noontime dataset is defined between LST from 10:00 to 14:00. Table 3 shows the correlation coefficients of different variables with microinverter temperature in different time periods [47, 48]. Fig 4(a) shows the variation in microinverter and PV module temperature with irradiance level for Q.t12 PV modules and microinverter in the morning. Under conditions of low irradiance in morning hours, ambient temperature has the strongest correlation with microinverter temperature (Table 3). We find that the temperature difference between the microinverter temperature and the ambient temperature is very small (approximately 0.40°C) when irradiance is below 60 W/m2. When the irradiance is greater than 60 W/m2, the PV modules are heating more dramatically in addition to producing more power. Consequently, the microinverters’ temperature also starts to increase. These results are summarized in Table 4 where ΔModule.T and ΔMicro.T stand for temperature differences between the PV module temperature and the ambient temperature, and the microinverter temperature and the ambient temperature respectively.


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)

Variation in microinverter temperature and PV module temperature with irradiance in (a) the morning, and (b) noon time.Variation in microinverter temperature and PV module temperature with irradiance for Q.t12 PV microinverter and PV modules in the morning and noon time.
© Copyright Policy
Related In: Results  -  Collection

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

pone.0131279.g004: Variation in microinverter temperature and PV module temperature with irradiance in (a) the morning, and (b) noon time.Variation in microinverter temperature and PV module temperature with irradiance for Q.t12 PV microinverter and PV modules in the morning and noon time.
Mentions: According to the distinct thermal characteristics of microinverters in different time ranges, we segregate two subsample data sets, dubbed “morning” and “noontime”, to isolate and observe the thermal attributes under relative low and high irradiance conditions. Morning time is defined as local solar time (LST) [49] from 05:00 to 06:30, and the noontime dataset is defined between LST from 10:00 to 14:00. Table 3 shows the correlation coefficients of different variables with microinverter temperature in different time periods [47, 48]. Fig 4(a) shows the variation in microinverter and PV module temperature with irradiance level for Q.t12 PV modules and microinverter in the morning. Under conditions of low irradiance in morning hours, ambient temperature has the strongest correlation with microinverter temperature (Table 3). We find that the temperature difference between the microinverter temperature and the ambient temperature is very small (approximately 0.40°C) when irradiance is below 60 W/m2. When the irradiance is greater than 60 W/m2, the PV modules are heating more dramatically in addition to producing more power. Consequently, the microinverters’ temperature also starts to increase. These results are summarized in Table 4 where ΔModule.T and ΔMicro.T stand for temperature differences between the PV module temperature and the ambient temperature, and the microinverter temperature and the ambient temperature respectively.

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