基于高斯核模糊C均值聚类的光伏阵列故障诊断方法

In the process of PV array fault diagnosis, the traditional characteristics are difficult to identify the single and some multi-faults with similar features, and the fault datasets collected in the operating condition of the field experiment are disturbed by the external environment in the meantime, these two reasons lead to the failure rate of the diagnostic accuracy. In order to conquer the problem, a new eigenvector including V_norm, I_norm and FF is proposed to characterize these fault conditions. This feature vector, combined with Gaussian kernel fuzzy C-means clustering method, is used to classify eight common faults in PV array is proposed in this paper. The combination of these three fault characteristics effectively reduces the impact of environmental conditions in the process of fault identification. The GKFCM has good performance for clustering the complex datasets, so it can effectively improve the recognition accuracy in the process of identify different fault classes with noise interference. The algorithm contains two phases: training and testing process. In the training phase, the fault datasets are clustered to obtain the center points and then the new fault data is classified as an existing fault type or a new fault kind by calculating the similarity between the existing center points and new fault data. This method can not only identify single fault condition but also detect multiple failure conditions. Finally, the Simulink simulation and the field experiment showed that the method can effectively detect several main types of PV array faults.