电力系统保护与控制2026，Vol.54Issue(8)：116-128,13.DOI:10.19783/j.cnki.pspc.251281
融合退化特征与随机效应逆高斯过程的光伏组件寿命预测方法
Lifetime prediction method for photovoltaic modules integrating degradation characteristics and random effects inverse Gaussian process
摘要
Abstract
To address the randomness,nonlinearity,and individual variability in the degradation process of photovoltaic(PV)modules caused by environmental factors,manufacturing processes,and installation conditions,a lifetime prediction method for PV modules integrating degradation characteristics and random effects inverse Gaussian process is proposed.First,a power degradation model for PV modules based on the random effects inverse Gaussian process is established to characterize the nonlinear and stochastic features of the module degradation process.Second,according to the degradation characteristics of PV modules,parameter estimation of the degradation model is performed using the Bayesian Markov Chain Monte Carlo method.Then,parameter sets are extracted from the posterior distribution via the Monte Carlo method to simulate the stochastic degradation paths of PV modules.The time when each degradation path first reaches the failure threshold is recorded to obtain the lifetime distribution and reliability function of the PV modules.Finally,simulation results demonstrate that,compared with lifetime prediction methods based on random nonlinear Gamma processes and exponential diffusion processes,the proposed model achieves a mean relative error as low as 2.8%and a maximum relative error of only 6.7%,demonstrating its effectiveness.关键词
光伏组件/寿命预测/退化特征/随机效应/逆高斯过程Key words
photovoltaic modules/lifetime prediction/degradation characteristics/random effects/inverse Gaussian process引用本文复制引用
裴婷婷,张方正,陈伟,吴阳..融合退化特征与随机效应逆高斯过程的光伏组件寿命预测方法[J].电力系统保护与控制,2026,54(8):116-128,13.基金项目
This work is supported by the National Natural Science Foundation of China(No.51767017). 国家自然科学基金项目资助(51767017) （No.51767017）
甘肃省联合科研基金重大项目资助(25JRRA1143) （25JRRA1143）
甘肃省兰州理工大学青年教师学科交叉研究培育项目资助(LUTXKJC-25001) （LUTXKJC-25001）
