Transmission line fault cause identification method based on transient waveform image and MCNN-LSTM

Abstract

Power system operators commonly employ fault recorders to record and analyze transmission line faults. However, the length of transmission lines, the number and location of power sources and the sampling frequency of fault recorder will influence the characteristics of the fault waveform leading to fault identification error. This paper proposed a multi-head convolutional neural network with long short-term memory (MCNN-LSTM) using transient waveform image as input for transmission line fault type and fault cause classification. First, the characteristics of transmission line faults were analyzed. Second, MCNN-LSTM is designed by considering interaction between electrical signal. Finally, the modified IEEE-39 node power network was adopted to test the proposed method. The test results show that: this method can improve the identification accuracy of transmission line faults even if there is class unbalance in fault sample set, and shows strong adaptability when transferred to small-size fault samples of transmission lines.