电力系统及其自动化学报2026，Vol.38Issue(4)：25-36,12.DOI:10.19635/j.cnki.csu-epsa.001641
深度生成数字孪生框架下的光伏阵列故障诊断
Fault Diagnosis of Photovoltaic Array Under Deep Generative Digital Twins Framework
摘要
Abstract
Aimed at the problems of poor feature extraction and suboptimal diagnostic performance under small-sample scenarios,which are caused by difficulty in obtaining the photovoltaic(PV)array fault data under real operating condi-tions,a deep generative digital twins-based PV array fault diagnosis method is proposed.First,a mechanism model is constructed using the parameters of an actual PV system to generate typical fault data,addressing the issue of data scar-city.Second,a recursive feature elimination in cross validation module based on least squares support vector machine is developed to automatically evaluate and select the key features,enhancing the features'effectiveness.Third,a one-dimensional Pearson correlation coefficient UNet diffusion model is proposed to learn the intrinsic distribution character-istics of fault data and enhance the data diversity.Finally,a stochastic configuration network optimized by the aquila op-timizer is introduced to achieve an accurate fault diagnosis based on the augmented data.The proposed method was ex-perimentally validated on a 250 kW grid-connected PV system,achieving a fault diagnosis accuracy of 97.9%.关键词
光伏阵列/数字孪生/故障诊断/随机配置网络Key words
photovoltaic(PV)array/digital twins/fault diagnosis/stochastic configuration network分类
信息技术与安全科学引用本文复制引用
甄成,刘利强,齐咏生,李永亭..深度生成数字孪生框架下的光伏阵列故障诊断[J].电力系统及其自动化学报,2026,38(4):25-36,12.基金项目
国家自然科学基金资助项目(62363029). （62363029）
