一种四参数的光伏组件在线故障诊断方法

发布时间：2018-03-20 19:49

本文选题：光伏组件　切入点：在线诊断　出处：《中国电机工程学报》2014年13期 　论文类型：期刊论文

【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。
[Abstract]:Analysis of PV modules in short circuit, the output characteristics of abnormal aging condition, put forward a kind of based on open circuit voltage, short-circuit current, PV module online diagnosis of short circuit voltage and current maximum power point four parameters and method of abnormal aging fault. The fault type of factor K, the difference judgment component through comparison with the standard value is K short circuit fault and abnormal aging. Failure can be carried out online fault analysis and early warning: when the fault, the number of battery short circuit method in neural network diagnostic module; abnormal aging fault, the fill factor value for component aging. Simulation and experimental results show that the method has high accuracy, proves the method the feasibility and effectiveness.

【作者单位】：上海市电站自动化技术重点实验室(上海大学);
【基金】：国家自然科学基金项目(51107079)~~
【分类号】：TM914

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