首页|基于I-V曲线逆推法的光伏组件故障诊断策略

基于I-V曲线逆推法的光伏组件故障诊断策略

扫码查看
光伏组件故障诊断一般依赖于对辐照度和温度等环境变量的测量,对分散的组件故障判别不够精确。文章提出了基于逆推I-V 曲线法的光伏组件故障诊断策略。该策略事先提取光伏组件模型参数,然后计算不同辐照度及太阳能电池温度下的I-V 曲线,形成I-V曲线库;在运行时无须实时监测太阳能电池运行时表面辐照度及平均温度,仅测量光伏组件的开路电压、短路电流和最大功率点电压、电流,即可判断出组件是否发生故障。搭建实验平台对典型故障进行模拟并利用该策略进行判别,结果表明,文章提出的策略能够有效判断组件的故障。利用该策略研发了单板故障监测模块,实现了光伏组件在线故障判断,提高了光伏组件故障判断的精确性及光伏电站运行的可靠性和经济性。
Fault diagnosis method of solar cell based on inverse inference of I-V curves
In this paper,a fault diagnosis strategy for photovoltaic modules based on I-V curve inverse method is proposed.This strategy does not need to monitor the surface irradiance and average temperature of the solar cell in real time.After extracting the model parameters,the I-V curve library under different irradiance and solar cell temperature is calculated.The open circuit voltage,short circuit current and maximum power point voltage and current of the photovoltaic module are measured during operation to determine whether the module is faulty.By building experimental equipment to simulate typical faults and using this method to judge,the results show that the method proposed in this paper can effectively monitor the faults of components.Using this method,a single-board fault monitoring module is developed to realize online fault diagnosis of photovoltaic modules,which improves the accuracy of fault diagnosis of photovoltaic modules and the reliability and economy of photovoltaic power station operation.

solar cellparameter identificationmaximum power pointinverse derivation of I-V curve

朱青云、刘凡、曾伟

展开 >

青海省产品质量检验检测院,青海 西宁 810003

南昌工程学院 电气工程学院,江西 南昌 330099

国网江西省电力有限公司电力科学研究院,江西 南昌 330096

太阳能电池 参数辨识 最大功率点 逆推I-V曲线

国家自然科学基金项目

52267007

2024

可再生能源
辽宁省能源研究所 中国农村能源行业协会 中国资源综合利用协会可再生能源专委会 中国生物质能技术开发中心 辽宁省太阳能学会

可再生能源

CSTPCD北大核心
影响因子:0.605
ISSN:1671-5292
年,卷(期):2024.42(8)