基于参数辨识的光伏组件故障诊断模型  

Parameter Identification Based Fault Diagnosis Model of Photovoltaic Modules

作　　者：韩伟[1] 王宏华[1] 王成亮[2] 陈凌[1] 张经炜[1] 孙蓉 

机构地区：[1]河海大学能源与电气学院,江苏省南京市211100 [2]江苏方天电力技术有限公司,江苏省南京市211102 [3]国网江苏省电力公司电力科学研究院,江苏省南京市211103

出　　处：《电网技术》2015年第5期1198-1204,共7页Power System Technology

基　　金："江苏省研究生培养创新工程(CXZZ12_0228)";"国网江苏省电力公司科技项目(J2014028)"的资助

摘　　要：为了对光伏组件运行状况进行准确判断,提出了一种基于参数辨识的组件故障诊断模型。分析了任意工况下的光伏组件输出特性曲线,借助于改进人工鱼群算法对数学模型中各参数进行了辨识。通过分析各模型参数随光照和温度的变化关系来获取多组工况下的模型参数值,结合光伏组件各种故障数据建立了以光生电流、二极管反向饱和电流、二极管理想品质因素和等效串并联电阻为输入层向量,以组件正常、组件短路、等效串联电阻异常老化和等效并联电阻异常老化为输出层向量的径向基函数(radical basis function,RBF)神经网络故障诊断仿真模型,仿真结果验证了上述光伏组件故障规律的正确性。搭建了基于可编程电子负载的光伏组件户外实验平台,进行了组件故障诊断的实验研究,实验结果验证了所提方法的有效性和准确性。To exactly judge the operating conditions of photovoltaic （PV） modules, a parameter identification based fault diagnosis model for PV modules is proposed. The output characteristic curves of PV modules under arbitrary operating modes are analyzed, and by means of improved artificial fish swarm algorithm, parameters in the mathematical model are identified. Through analyzing the variation of the model parameters with the change of irradiance and temperature the model parameter values under multiple operating modes are obtained, and according to various fault data of PV modules a radical basis function （RBF） neural network based fault diagnosis model, in which the photo-generated current, the reverse saturation current of the diode, the ideal quality factor of the diode and equivalent series-parallel resistance are taken as the input layer vectors and the normality of the module, the short-circuit of the module, the abnormal ageing of equivalent series resistance and the abnormal ageing of equivalent series-parallel resistance are taken as output layer vectors, is established, and the correctness of above-mentioned fault rule of PV modules is validated by simulation results. A programmable electronic load based outdoor experimental platform for PV module is constructed to carry out experimental research on fault diagnosis Of PV modules. The effectiveness and accuracy of the proposed method are validated by experimental results.

关 键 词：光伏组件 参数辨识 故障诊断 改进人工鱼群算法 RBF神经网络 

分 类 号：TM615[电气工程—电力系统及自动化]