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Fault detection in 3-phase Traction Motor using Artificial Neural Networks

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4 Author(s)
Seyed Saeid Moosavi ; SeT Laboratory, Université de Technologie de Belfort, Monbéliard (UTBM), France ; Abdesslem. Djerdir ; Youcef. Aït-Amirat ; Davod. Arab. Khaburi

Traction Motors Condition Monitoring is one of the important factors in increasing motor life time and prevention of any train sudden stop in track and thereupon avoiding interruptions in track traffic. In this paper, a neural network based method for detecting unbalanced voltage fault which is one of the various faults in 3-phase traction motors was surveyed. Proposed method is independent from load state and fault percentage; which means neural network is able to detect fault and load condition without any assumption about the state of the load and fault. In proposed method, two separate neural networks are used for each problem. Experimental acquired data is used to train neural networks. Based on first test results, the neural structure could detect unbalanced voltage fault percentage with 98.5% precision. Also, based on second test results, the neural network could detect load condition accurately in 97% of the cases. According to these results, neural network is a good choice for solving similar problems.

Published in:

2012 IEEE Transportation Electrification Conference and Expo (ITEC)

Date of Conference:

18-20 June 2012