东北电力技术2025，Vol.46Issue(8)：15-20,6.
基于神经网络的光伏组件故障分析系统研究设计
Research and Design on Neural Network-Based Fault Analysis System for Photovoltaic Modules
摘要
Abstract
With the large-scale application of photovoltaic modules,the update and maintenance of photovoltaic modules become a problem that cannot be ignored.In the use of photovoltaic modules,heat spots caused by failure,aging caused by daily use,and damage caused by external forces such as weather factors will cause abnormal operation.Firstly,the states of photovoltaic modules are roughly divided into normal,aging state,open circuit fault,short circuit fault,local shade.In order to make repair plan or replace-ment plan of photovoltaic module more accurately,it optimizes the traditional neural network fault analysis strategy.Secondly,by in-troducing the feature importance analysis of the random forest algorithm,it adds the fill coefficient,the slope of the U-I curve and the power parameters to improve the accuracy of the neural network in fault judgment.At the same time,it uses the genetic algorithm to optimize the BP neural network algorithm and solve the problem that the accuracy of accuracy is poor and it is easy to fall into the local optimal.Finally,the experimental results show that after introducing these parameters,the accuracy of fault type determination is im-proved.The error between the theoretical estimate and the actual result is reduced by 5%～11%,and the optimized diagnosis accuracy is increased by 2.4%compared with the traditional BP neural network fault diagnosis model.关键词
光伏组件/故障检测/BP神经网络/故障诊断Key words
photovoltaic module/fault detection/BP neural network/fault diagnosis分类
信息技术与安全科学引用本文复制引用
周嘉泽,薛志华,刘彦滨,臧德华,王立地..基于神经网络的光伏组件故障分析系统研究设计[J].东北电力技术,2025,46(8):15-20,6.基金项目
辽宁省科学研究经费项目(LJKMZ20221032) （LJKMZ20221032）
内蒙古自治区科技创新引导奖励资金项目(CXYD2022004) （CXYD2022004）
