Remaining Useful Life Prediction of PV Systems Under Dynamic Environmental Conditions

Abstract

Solar power is one of the least carbon-intensive approaches for electricity generation, and so photovoltaic (PV) systems have great potential as a low-carbon technology during their long lifecycle. Consequently, remaining useful life (RUL) prediction is critical for the prognostics and health management of PV systems, potentially preventing unexpected failure and maintenance due to PV degradation. One of the major root causes of PV degradation is the dynamic environmental conditions associated with PV outdoor operation. However, RUL prediction under such dynamic environmental conditions remains challenging. This article presents a semiparametric prognostic framework for PV systems under dynamic environmental conditions. The quantitative relationship between PV degradation and environmental conditions is established to integrate environmental condition information into RUL prediction, combining the cumulative damage model with multivariate Bernstein bases. The block bootstrap method is used to estimate future environmental conditions as inputs for RUL prediction. The least-squares estimators of the model parameters can be obtained through the block coordinate descent method. Finally, applications to field data of Australian PV systems are presented to demonstrate the effectiveness of the proposed method. The proposed framework is applicable to most PV technologies.