一种四参数的光伏组件在线故障诊断方法  

A Survey of Online Fault Diagnosis for Photovoltaic Modules Based on Four Parameters

作　　者：王元章[1] 李智华[1] 吴春华[1] 

机构地区：[1]上海市电站自动化技术重点实验室(上海大学),上海市闸北区200072

出　　处：《中国电机工程学报》2014年第13期2078-2087,共10页Proceedings of the CSEE

基　　金：国家自然科学基金项目(51107079)~~

摘　　要：分析光伏组件在短路、异常老化状态下的输出特性,提出一种基于开路电压、短路电流、最大功率点电压和电流四参数的光伏组件在线诊断短路及异常老化故障的方法。建立了故障类型因子K,通过比较K与标准值的差异判断组件是否存在短路和异常老化故障。发生故障即可进行在线故障程度分析和预警:短路故障时,利用神经网络方法诊断组件中电池短路的块数;异常老化故障时,利用填充因子值获得组件老化程度。仿真及实验结果显示该方法具有较高的准确率,证明了方法的可行性和有效性。The output characteristics of photovoltaic （PV） modules under short-circuit or abnormal degradation conditions were analyzed. The online fault diagnosis method for PV modules based on four parameters, namely open circuit voltage, short-circuit current, the voltage of maximum power point and the current of maximum power point were proposed. Then the fault type factor K was introduced. Through comparing the difference between the value of K and the standard value, the type of faults could be determined. Once the faults are certain, the extent of faults and the early warnings are analyzed automatically online. When the PV modules are short-circuited, the artificial neural network can be utilized for acquiring the number of short-circuited ceils. When the PV modules are in abnormal degradation, the value of fill factor （FF） can be utilized to acquire the extent of degradation. The results of simulations and experiments show that the method has a high accuracy rate, and its feasibility and effectiveness are proved.

关 键 词：光伏组件 在线诊断 异常老化 神经网络 故障类型因子 

分 类 号：TM91[电气工程—电力电子与电力传动]