Impact Of Duration And Missing Data On The Long-Term Photovoltaic Degradation Rate Estimation

Accurate quantification of photovoltaic (PV) system degradation rate (RD) is essential for lifetime yield predictions. Although R-D is a critical parameter, its estimation lacks a standardized methodology that can be applied on outdoor field data. The purpose of this paper is to investigate the impact of time period duration and missing data on R-D by analyzing the performance of different techniques applied to synthetic PV system data at different linear R-D patterns and known noise conditions. The analysis includes the application of different techniques to a 10-year synthetic dataset of a crystalline Silicon PV system, with emulated degradation levels and imputed missing data. The analysis demonstrated that the accuracy of ordinary least squares (OLS), year-on-year (YOY), autoregressive integrated moving average (ARIMA) and robust principal component analysis (RPCA) techniques is affected by the evaluation duration with all techniques converging to lower R-D deviations over the 10-year evaluation, apart from RPCA at high degradation levels. Moreover, the estimated R-D is strongly affected by the amount of missing data. Filtering out the corrupted data yielded more accurate R-D results for all techniques. It is proven that the application of a change-point detection stage is necessary and guidelines for accurate R-D estimation are provided. (C) 2021 The Authors. Published by Elsevier Ltd.