Periodical evaluation of photovoltaic modules and diode parameter extraction method using multiple linear regression models

Abstract

Abstract The stability and performance of photovoltaic (PV) modules can be assessed by outdoor testing where external conditions such as illumination and module temperature are measured at regular time intervals along with the jV-curve of the module. However, the fluctuation and seasonal variation of external conditions can make it difficult to trace changes such as degradation in PV-module properties (at e.g. standard test conditions). This contribution demonstrates the use of multiple linear regressions (MLR) to overcome these difficulties. The data gathered over large periods is condensed into a set of few predictors, that reproduce the jV parameters at infrequently encountered conditions that are required for comparison. Furthermore, the parameters of a physical device model are calculated directly from MLR-predictors, validating our procedure two-fold, by applying the MLR-method to simulated data, replicating the original input parameters, and comparing monthly parameter averages between the MLR-method and a known parameter extraction method.