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OA

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.