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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

PV module and microinverter setup in a dual-axis tracker in SDLE SunFarm.PV module and microinverter setup in a dual-axis tracker with thermocouple attached to the hottest points of the PV module and the microinverter.
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pone.0131279.g001: PV module and microinverter setup in a dual-axis tracker in SDLE SunFarm.PV module and microinverter setup in a dual-axis tracker with thermocouple attached to the hottest points of the PV module and the microinverter.

Mentions: A Kipp & Zonen CMP11 pyranometer was used to measure insolation data and a Vaisala WXT520 weather transmitter collected the environmental data: ambient temperature, wind speed, wind direction, relative humidity, and rain intensity. T-type thermocouples (CO1-T) from Omega Engineering Inc. were used to measure the PV module backsheet temperature and the microinverter peak temperature. The thermocouples were attached to the middle of the backsheet of the PV modules and the hottest point on the microinverters (Fig 1) as determined by infrared (IR) thermography collected at the maximum rated input power using a FLIR T300 camera [38]. The pyranometer, weather transmitter and the thermocouples reports the data to the Campbell CR1000 data loggers [39] and multiplexers [40] at a one minute time interval. The data loggers store all the collected data in the central database every two hours. An Enphase Envoy device maintains power line communication with the Enphase microinverters to collect the inverter telemetry: DC voltage and current, and AC power, frequency, and microinverter internal temperature and reports those data to the Enphase enlighten website every 5 minutes. The power data was automatically acquired from the Enphase enlighten website using the Java Selenium web driver package and stored in SDLE Center’s local file-store. Later, all these data: power, insolation, temperature and climate, were ingested into SDLE center’s informatics and analytics infrastructure, known as Energy CRADLE [41]. Data visualization and analytics in this manuscript were generated using ‘R’ open-source software [42, 43].


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)

PV module and microinverter setup in a dual-axis tracker in SDLE SunFarm.PV module and microinverter setup in a dual-axis tracker with thermocouple attached to the hottest points of the PV module and the microinverter.
© Copyright Policy
Related In: Results  -  Collection

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

pone.0131279.g001: PV module and microinverter setup in a dual-axis tracker in SDLE SunFarm.PV module and microinverter setup in a dual-axis tracker with thermocouple attached to the hottest points of the PV module and the microinverter.
Mentions: A Kipp & Zonen CMP11 pyranometer was used to measure insolation data and a Vaisala WXT520 weather transmitter collected the environmental data: ambient temperature, wind speed, wind direction, relative humidity, and rain intensity. T-type thermocouples (CO1-T) from Omega Engineering Inc. were used to measure the PV module backsheet temperature and the microinverter peak temperature. The thermocouples were attached to the middle of the backsheet of the PV modules and the hottest point on the microinverters (Fig 1) as determined by infrared (IR) thermography collected at the maximum rated input power using a FLIR T300 camera [38]. The pyranometer, weather transmitter and the thermocouples reports the data to the Campbell CR1000 data loggers [39] and multiplexers [40] at a one minute time interval. The data loggers store all the collected data in the central database every two hours. An Enphase Envoy device maintains power line communication with the Enphase microinverters to collect the inverter telemetry: DC voltage and current, and AC power, frequency, and microinverter internal temperature and reports those data to the Enphase enlighten website every 5 minutes. The power data was automatically acquired from the Enphase enlighten website using the Java Selenium web driver package and stored in SDLE Center’s local file-store. Later, all these data: power, insolation, temperature and climate, were ingested into SDLE center’s informatics and analytics infrastructure, known as Energy CRADLE [41]. Data visualization and analytics in this manuscript were generated using ‘R’ open-source software [42, 43].

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