基于三参数的光伏组件老化程度诊断  

Diagnosing the Aging Degree of Photovoltaic Modules Based on Three Parameters

作　　者：李智华[1] 马浩强 吴春华[1] 汪飞[1] 俞薛颖 LI Zhihua;MA Haoqiang;WU Chunhua;WANG Fei;YU Xueying(Shanghai Key Laboratory of Power Station Automation Technology(Shanghai University),Baoshan District,Shanghai 200444,China)

机构地区：[1]上海市电站自动化技术重点实验室(上海大学),上海市宝山区200444

出　　处：《中国电机工程学报》2022年第9期3327-3337,共11页Proceedings of the CSEE

基　　金：国家自然科学基金项目(51677112)。

摘　　要：为了对光伏组件老化程度进行有效评估与诊断,提出基于三参数的光伏组件老化指标来定量判断老化程度的方法。依据光伏组件的输出I-V特性,通过一种改进的量子粒子群算法来辨识光伏组件老化三参数,即光生电流、串联电阻与并联电阻;再将辨识参数映射至标况参数;最后使用概率神经网络进行组件老化指标计算,获得老化程度的量化值。仿真和实验表明,该方法可以对不同环境条件下的光伏组件的老化程度进行有效诊断,可以为光伏组件故障预警及寿命预测提供参考。In order to evaluate and diagnose the aging degree of photovoltaic modules effectively,a quantitative method of judging the aging degree of photovoltaic modules based on the aging index of photovoltaic modules with three parameters was proposed.According to the output I-V characteristics of photovoltaic modules,a modified quantum particle swarm optimization(QPSO)algorithm was used to identify three aging parameters of photovoltaic modules,i.e.,photogenerated current,series resistance,and parallel resistance.Then,the identification parameters were mapped to the standard test condition parameters.Finally,probabilistic neural network(PNN)was used to calculate the aging index of components and obtain the quantitative value of aging degree.Simulation and experiment results show that this method can effectively diagnose the aging degree of photovoltaic modules under different environmental conditions,and can provide reference for the fault pre-warning and lifetime forecast of photovoltaic modules.

关 键 词：光伏组件 老化参数 老化指标 老化程度诊断 量子粒子群 概率神经网络 

分 类 号：TM61[电气工程—电力系统及自动化]