Predictive linear regression model for microinverter internal temperature

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.