A Fault Identification Method of Hybrid HVDC System Based on Wavelet Packet Energy Spectrum and CNN
Yan Liang, Junwei Zhang, Zheng Shi, Haibo Zhao, Yao Wang, Yahong Xing, Xiaowei Zhang, Yujin Wang, Haixiao Zhu
Abstract
Aiming at the shortcomings of traditional fault identification methods in fault information acquisition, In the scenario of hybrid HVDC transmission system, a new fault identification method is proposed by using wavelet packet energy spectrum and convolutional neural network (CNN), which effectively solves the problem of complex fault feature extraction of hybrid HVDC transmission system. This method effectively improves the accuracy of fault identification. Firstly, tThe frequency-domain characteristics of the fault transient signal are extracted by wavelet packet transform, and the feature differences are reflected in the form of energy spectrum. Secondly, according to the extracted energy feature information, the order of fault line and fault type is identified by CNN. Finally, through example verification and algorithm comparison, it is concluded that, the mentioned model has a strong ability to identify faults, and has strong anti-noise interference and tolerance to transition resistance.