光伏组件Ⅰ-Ⅴ输出特性的典型故障分析与诊断  

Typical fault analysis and diagnosis based on I-V output characteristics of photovoltaic modules

作　　者：高剑 郭倩 卫东 GAO Jian;GUO Qian;WEI Dong(College of Mechanical and Electrical Engineering,China Jiliang University,Hangzhou 310018,China;College of Modern Science and Technology,China Jiliang University,Hangzhou 310018,China)

机构地区：[1]中国计量大学机电工程学院,浙江杭州310018 [2]中国计量大学现代科技学院,浙江杭州310018

出　　处：《中国测试》2024年第12期163-168,共6页China Measurement & Test

基　　金：浙江省属高校基本科研业务费专项资金资助项目(2021YW42);浙江省基础公益研究计划(LGG22E070003,LGG20E070003)。

摘　　要：通过基于单二极管太阳电池等效电路模型,研究分析热斑、电势诱导衰减(potential induced degradation,PID)和老化3种典型故障的机理特性与Ⅰ-Ⅴ输出特性。通过分析3种典型故障在Ⅰ-Ⅴ特性曲线中的变化规律及差异性,提取不同故障的关键特征量,并将特征量与概率神经网络(probabilistic neural network,PNN)相结合,提出一种基于Ⅳ输出特性的故障诊断方法。通过光伏发电运营企业提供的故障组件作为实验数据来源进行实验测试,实验结果表明:故障诊断模型的准确率为99.02%左右,仅存在少有的误判情况,所提出的故障诊断方法能够可靠地实现各故障的原因判定,可对智能化地运维光伏电站提供有效的帮助。Based on the equivalent circuit model of a single diode solar cell,the mechanism characteristics and I-V output characteristics of three typical faults,namely hot spot,potential induced attenuation(potential induced degradation,PID),and aging,are studied and analyzed.Based on the variation characteristics of the I-V characteristic curve of the three typical faults,diagnostic feature quantities for different faults are extracted,and a fault diagnosis method based on I-V output characteristics is proposed by combining the feature quantities with probability neural network(probabilistic neural network,PNN).Conduct experimental testing using faulty components provided by photovoltaic power generation operation enterprises as experimental data sources,the experimental results show that the accuracy of the fault diagnosis model is about 99.02%,with only a few cases of misjudgment.The proposed fault diagnosis method can reliably determine the cause of each fault,providing effective assistance for intelligent operation and maintenance of photovoltaic power plants.

关 键 词：太阳电池 光伏组件 等效电路模型 Ⅰ-Ⅴ输出特性 特征量提取 故障诊断 

分 类 号：TB9[一般工业技术—计量学] TM615[机械工程—测试计量技术及仪器]