Data-driven measurement performance evaluation of voltage transformers in electric railway traction power supply systems

Abstract

Critical for metering and protection in electric railway traction power supply systems (TPSSs), the measurement performance of voltage transformers (VTs) must be timely and reliably monitored. This paper outlines a three-step, RMS data only method for evaluating VTs in TPSSs. First, a kernel principal component analysis approach is used to diagnose the VT exhibiting significant measurement deviations over time, mitigating the influence of stochastic fluctuations in traction loads. Second, a back propagation neural network is employed to continuously estimate the measurement deviations of the targeted VT. Third, a trend analysis method is developed to assess the evolution of the measurement performance of VTs. Case studies conducted on field data from an operational TPSS demonstrate the effectiveness of the proposed method in detecting VTs with measurement deviations exceeding 1% relative to their original accuracy levels. Additionally, the method accurately tracks deviation trends, enabling the identification of potential early-stage faults in VTs and helping prevent significant economic losses in TPSS operations.

Keywords

Voltage transformer / Traction power supply system / Measurement performance / Data-driven evaluation / Abrupt change detection / Bootstrap confidence interval

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Funding

National Natural Science Foundation of China http://dx.doi.org/10.13039/501100001809(52107125); Applied Basic Research Project of Sichuan Province(2022NSFSC0250); The funding of Chengdu Guojia Electrical Engineering Co.,Ltd(KYL202312-0043)