Abstract：Aiming at the problem of photovoltaic system fault classification, a fault classification method that combines wavelet packet transform and random forest algorithm is proposed. The fault voltage data of the photovoltaic system are first collected, then the wavelet packet transform is used to decompose the voltage signal, the energy of each frequency band is extracted as the fault feature, and the feature samples are sent into the random forest algorithm for classification. The random forest algorithm is a algorithm that combines ensemble learning theory and random subspace method, which can accurately classify various faults. The independent photovoltaic power generation system is built by PSCAD/EMTDC, 12 types of faults are selected for simulation, 600 samples of fault feature are obtained, among which 360 samples are used to train the random forest classifier, and 240 samples are used to test the classification performance of the classifier. The simulation results show that this method can effectively identify 12 types of faults in the photovoltaic system, and the classification accuracy rate reaches 97.92%. Compared with the RBF neural network, the fault classification accuracy rate is increased by 4.17%, which have important meaning for the further realization of photovoltaic system fault diagnosis research.
吴忠强,曹碧莲,侯林成,马博岩,胡晓宇. 基于小波包变换和随机森林算法的光伏系统故障分类[J]. 计量学报, 2021, 42(12): 1649-1656.
WU Zhong-qiang,CAO Bi-lian,HOU Lin-cheng,MA Bo-yan,HU Xiao-yu. A Fault Classification Method of Photovoltaic Systems Based on Wavelet Packet Transform and Random Forest. Acta Metrologica Sinica, 2021, 42(12): 1649-1656.
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2021, Vol. 42 
