Predictive linear regression model for microinverter internal temperature

Author

Hossain, Md Aynal ; Peshek, Timothy J. ; Yifan Xu ; Yang Hu ; Liang Ji ; Abramson, Alexis R. ; French, R.H.

Author_Institution

Dept. of Mech. & Aerosp. Eng., Case Western Reserve Univ., Cleveland, OH, USA

fYear

2014

fDate

8-13 June 2014

Abstract

A predictive linear regression model was developed to predict the microinverter internal temperature operating under real-world conditions on dual-axis trackers. The predictive model is a function of statistically significant variables: ambient temperature, photovoltaic (PV) module temperature, irradiance and AC power data. Time-series environmental, temperature and power data were analyzed in a statistical analytical approach to identify the statistically significant variables. The adjusted r-squared value of the predictive model is 0.9793. The dominant contributor to the microinverter temperature is the PV module backsheet temperature.

Keywords

invertors; photovoltaic power systems; regression analysis; statistical analysis; PV module backsheet temperature; adjusted r-squared value; ambient temperature; dual-axis tracker; irradiance; microinverter internal temperature; photovoltaic module temperature; predictive linear regression model; statistical analytical approach; statistically significant variable; time-series environmental AC power data; Correlation; Linear regression; Predictive models; Temperature distribution; Temperature measurement; Wind speed; microinverters; photovoltaic systems; reliability; temperature prediction;

fLanguage

English

Publisher

ieee

Conference_Titel

Photovoltaic Specialist Conference (PVSC), 2014 IEEE 40th

Conference_Location

Denver, CO

Type

conf

DOI

10.1109/PVSC.2014.6925081

Filename

6925081