Research on Fault Location Method of Track Circuit Compensation Capacitor Based on Probabilistic Neural Network

Journal: Architecture Engineering and Science DOI: 10.32629/aes.v3i2.822

Yichen Li, Zhiqiang Rao, Ziyi Li, Lu Ding

Urban Rail Transit and Logistics College, Beijing Union University, Beijing 100101, China

Abstract

Compensation capacitor is an important component for extending the signal transmission of track circuit, and its safe operation is very important to the transportation business of rail transit. According to the difficulty of diagnosing the fault of compensation capacitor, a fault location model of compensation capacitor based on probabilistic neural network is established. Firstly, the influence of compensation capacitors on the current curve is analyzed from the two aspects of the failure reasons of compensation capacitors and the influence on signal transmission. Then, according to the parameters of the track circuit, the important characteristic parameters affecting the compensation capacitors are screened. According to 4 different failure modes, a fault diagnosis model based on probabilistic neural network is constructed, and the BP neural network model is selected as the comparison experiment. The results show that the compensation capacitor fault location model based on probabilistic neural network has higher relative prediction accuracy and the shortest time.

Keywords

compensation capacitor, probabilistic neural network, fault location, track circuit

Funding

Science and technology research and development project of China State Railway Group (K2019Z006); the subject of science and technology development plan of the electrification company of China Railway Electrification Engineering Group Co., Ltd.

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