Open Access

ARTICLE

Remaining Life Prediction Method for Photovoltaic Modules Based on Two-Stage Wiener Process

by Jie Lin^*, Hongchi Shen, Tingting Pei, Yan Wu

School of Electrical Engineering and Information Engineering, Lanzhou University of Technology, Lanzhou, 730050, China

* Corresponding Author: Jie Lin. Email:

Energy Engineering 2025, 122(1), 331-347. https://doi.org/10.32604/ee.2024.055611

Received 02 July 2024; Accepted 21 October 2024; Issue published 27 December 2024

Abstract

Photovoltaic (PV) modules, as essential components of solar power generation systems, significantly influence unit power generation costs. The service life of these modules directly affects these costs. Over time, the performance of PV modules gradually declines due to internal degradation and external environmental factors. This cumulative degradation impacts the overall reliability of photovoltaic power generation. This study addresses the complex degradation process of PV modules by developing a two-stage Wiener process model. This approach accounts for the distinct phases of degradation resulting from module aging and environmental influences. A power degradation model based on the two-stage Wiener process is constructed to describe individual differences in module degradation processes. To estimate the model parameters, a combination of the Expectation-Maximization (EM) algorithm and the Bayesian method is employed. Furthermore, the Schwarz Information Criterion (SIC) is utilized to identify critical change points in PV module degradation trajectories. To validate the universality and effectiveness of the proposed method, a comparative analysis is conducted against other established life prediction techniques for PV modules.

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