电源技术2018，Vol.42Issue(4)：532-535,4.
基于KELM的光伏组件故障诊断方法
Fault diagnosis method for photovoltaic module based on KELM
摘要
Abstract
A fault diagnosis method for photovoltaic module based on kernel extreme learning machine (KELM) was presented.The relationship between the fault of various types of photovoltaic modules and photovoltaic module equivalent model parameters were analyzed.The optimal root mean square error (RMSE) for parameter identification was introduced as the characteristic variable of the local intrinsic shadow diagnosis,and the input characteristic vector of KELM fault diagnosis model was formulated and optimized.The simulation model and experimental analysis show that compared to the method directly using the equivalent model parameters as the input to the neural network,the proposed method can be more rapid and precise to identify the conventional short circuit,aging and shadow fault of the photovoltaic module.关键词
光伏组件/模型参数/故障诊断/KELMKey words
PV module/model parameter/fault diagnosis/KELM分类
信息技术与安全科学引用本文复制引用
吴越,陈志聪,吴丽君,林培杰..基于KELM的光伏组件故障诊断方法[J].电源技术,2018,42(4):532-535,4.基金项目
国家自然科学基金(61601127,51508105) （61601127,51508105）
福建省科技厅高校产学合作项目(2016H6012) （2016H6012）
福建省自然科学基金(2015J05124) （2015J05124）
福建省科技厅工业引导性重点项目(2015H0021,2016H0016) （2015H0021,2016H0016）
