基于改进RBF神经网络的光伏组件故障诊断

由于光伏组件的输出特性受多种因素混合影响,对光伏组件的故障检测是一个严峻的考验.为了保证故障诊断的实时性和精确性,采用多传感器法提取短路和开路故障特征,利用电压扫描法获取不均匀光照引起的热击穿和电击穿故障的判断依据,以故障特征为判据,给出一种基于K均值聚类算法的改进RBF神经网络的光伏组件故障诊断方法,在Matlab平...

Fault diagnosis of photovoltaic modules based on improved RBF neural network

The output characteristics of photovoltaic modules are affected by many factors, so the fault detection of photovoltaic modules is a severe test. In order to ensure the real-time and accuracy of fault diagnosis, multi-sensor method is used to extract short-circuit and open-circuit fault features, and voltage scanning method is used to obtain the judgment basis of thermal breakdown and electrical breakdown caused by uneven illumination. Based on fault characteristics, an improved RBF neural network based on K-means clustering is proposed for photovoltaic module fault diagnosis. The types of faults occurring in components and fault location are carried out, and the results are compared with those of BP neural network, which verifies the accuracy and effectiveness of the improved RBF neural network fault diagnosis method.