A Robust Data-driven Fault Detection Framework for Traction Dual Rectifiers

Qingli Deng, Shuai Zhao, Bin Gou, Xinglai Ge, Xiaoyun Feng, Huai Wang

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

Abstract

A framework to analyze the impact of feature selection, threshold level, and applied data window on fault detection accuracy is proposed in this study. It aims to bring a step closer to state-of-the-art fault detection research to practical applications. A relatively comprehensive database is established by emulating traction dual rectifiers for railway applications under diverse normal and fault operations. Factors such as grid-side fluctuations and harmonics, load changes and parameter variances, and a broad range of traction operation modes are considered in the database preparation. The proposed framework makes it possible to evaluate the accuracy with different design variables in the fault detection implementation. The presented study is exemplified by a case study on open-circuit faults in power devices of the traction dual rectifiers.
Original languageEnglish
Title of host publication2024 IEEE 10th International Power Electronics and Motion Control Conference (IPEMC2024-ECCE Asia)
Number of pages6
PublisherIEEE (Institute of Electrical and Electronics Engineers)
Publication date20 May 2024
Pages4481-4486
Article number10567256
ISBN (Print)979-8-3503-5134-7
ISBN (Electronic)979-8-3503-5133-0
DOIs
Publication statusPublished - 20 May 2024
Event2024 IEEE 10th International Power Electronics and Motion Control Conference (IPEMC2024-ECCE Asia) - Chengdu, China
Duration: 17 May 202420 May 2024

Conference

Conference2024 IEEE 10th International Power Electronics and Motion Control Conference (IPEMC2024-ECCE Asia)
LocationChengdu, China
Period17/05/202420/05/2024

Keywords

  • Accuracy
  • Databases
  • Fault detection
  • Feature extraction
  • Fluctuations
  • Rectifiers
  • Systems operation
  • diagnosis accuracy
  • threshold level
  • feature selection

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