Fault Diagnosis of Traction Transformer Based on Bayesian Network

Abstract

As the core equipment of a traction power supply system, the traction transformer is very important to ensure the safe and reliable operation of the system. At present, the three-ratio method is mainly used to distinguish transformer faults, whereas such a method has some defects, such as insufficient coding and over-general fault classification. At the same time, on-site maintenance personnel make an empirical judgment based on various test data, which is subjective and uncertain to a certain extent. For cases with multiple abnormal data and relatively complex conditions, on-site personnel often need to discuss and even dismantle the transformer to identify the fault, which is time-consuming and costly. In order to improve the effect of fault diagnosis for traction transformer, this paper uses Bayesian network to correlate the cause and effect of various tests and faults. By combining the results of field tests, the fault is diagnosed by the causal probability of the Bayesian network, rather than relying on the exception that occurred in a single experiment to judge its fault. The diagnosis results are more accurate and objective by using the Bayesian network. In this paper, the frequent test anomalies of the traction transformer are taken into account in the network, so that the network can more comprehensively analyze the operation situation of the traction transformer and judge the type of fault. According to field situations, based on the existing set of symptoms of the Bayesian network fault diagnosis, this paper further considers the insulation resistance, dielectric loss tangent value, oil and gas, power frequency voltage, and leakage current. By combining the association rules algorithm and the experience of the field operators, the cause–effect relationship of test data and the conditional probability parameters of the network are obtained. Then, the Bayesian network is constructed and used for traction transformer fault diagnosis. The case study shows that the four types of fault diagnosed using the Bayesian network model proposed in this paper are consistent with the fault types inspected by on-site operators, which shows promising engineering application prospects.