Transient Fault Signal Identification of AT Traction Network Based on Improved HHT and LSTM Neural Network Algorithm

Abstract

This paper aims to address the difficult to pinpoint fault cause of the full parallel AT traction power supply system with special structure. The fault characteristics are easily covered up, and high transition impedance only affects the singularity of the wavehead, making the traveling waves hard to identify. Moreover, the classification accuracy of the traditional time-frequency analysis method is not sufficiently high to distinguish precisely. In this paper, a fault classification method of traction network based on single-channel improved Hilbert–Huang transform and deep learning is proposed. This method extracts effective fault features directly from the original fault signals and classifies the fault types at the same time. The accuracy of data categorization is increased by directly applying the Hilbert–Huang transform to fault signals to extract transient fault features and produce one-dimensional feature data, which are analyzed by the time-frequency energy spectrum. Using the similarity recognition method of long-short-term memory neural network, the extracted high-frequency one-dimensional feature data are trained and tested to classify fault signals more accurately. In order to verify the effectiveness of this method, several kinds of short-circuit and lightning strike faults are continuously simulated and verified in this paper. Considering various fault conditions and factors, the proposed improved HHT+LSTM method is compared with the LSTM method for direct processing of the original signals. The improved HHT + LSTM classification algorithm achieves an accuracy of 99.99%.