A data-driven ground fault detection and isolation method for main circuit in railway electrical traction system

Due to the complex and harsh operation conditions, like corrosion, aging cable and static electricity, of electrical traction drive system, ground fault will generate large short circuit current to harm the key components. Effective fault diagnosis is important, but also challenging. The conventional method used for ground fault detection only takes advantage of voltage measurements of DC-link. Other measurements onboard are also available, which are correlated with the voltage measurements. Taking the correlation into account will improve the detection performance. To this end, this paper presents a data-driven solution, which makes full use of the correlation between the voltage measurements with other measurements onboard. The proposed method consists of two components: (1) a canonical correlation analysis-based fault detection method, which takes into account the correlation within measurements; (2) a fault isolation method by means of the fault direction, which can be obtained with the available faulty data stored in the long-term operation. The developed method is applied to a traction drive system. It is shown that the proposed approach is able to improve the fault detection and isolation performance significantly with respect to three performance indicators, namely fault detection rate, detection delay and correct isolation rate, in comparison with the conventional method, which only uses the voltage measurements of DC-link. (C) 2018 ISA. Published by Elsevier Ltd. All rights reserved.