Transient Fault Signal Identification of AT Traction Network Based on Improved HHT and LSTM Neural Network Algorithm

Huan Zhou, Jianyun Chen (), Manyuan Ye, Qincui Fu and Song Li
Additional contact information
Huan Zhou: State Key Laboratory of Performance and Guarantee of Rail Transportation Infrastructure, East China Jiaotong University, Nanchang 330013, China
Jianyun Chen: State Key Laboratory of Performance and Guarantee of Rail Transportation Infrastructure, East China Jiaotong University, Nanchang 330013, China
Manyuan Ye: State Key Laboratory of Performance and Guarantee of Rail Transportation Infrastructure, East China Jiaotong University, Nanchang 330013, China
Qincui Fu: State Key Laboratory of Performance and Guarantee of Rail Transportation Infrastructure, East China Jiaotong University, Nanchang 330013, China
Song Li: State Key Laboratory of Performance and Guarantee of Rail Transportation Infrastructure, East China Jiaotong University, Nanchang 330013, China

Energies, 2023, vol. 16, issue 3, 1-21

Abstract: This paper aims to address the difficult to pinpoint fault cause of the full parallel AT traction power supply system with special structure. The fault characteristics are easily covered up, and high transition impedance only affects the singularity of the wavehead, making the traveling waves hard to identify. Moreover, the classification accuracy of the traditional time-frequency analysis method is not sufficiently high to distinguish precisely. In this paper, a fault classification method of traction network based on single-channel improved Hilbert–Huang transform and deep learning is proposed. This method extracts effective fault features directly from the original fault signals and classifies the fault types at the same time. The accuracy of data categorization is increased by directly applying the Hilbert–Huang transform to fault signals to extract transient fault features and produce one-dimensional feature data, which are analyzed by the time-frequency energy spectrum. Using the similarity recognition method of long-short-term memory neural network, the extracted high-frequency one-dimensional feature data are trained and tested to classify fault signals more accurately. In order to verify the effectiveness of this method, several kinds of short-circuit and lightning strike faults are continuously simulated and verified in this paper. Considering various fault conditions and factors, the proposed improved HHT+LSTM method is compared with the LSTM method for direct processing of the original signals. The improved HHT + LSTM classification algorithm achieves an accuracy of 99.99%.

Keywords: traction power supply system; transient fault signal; Hilbert–Huang transform; long-short-term memory (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/16/3/1163/pdf (application/pdf)
https://www.mdpi.com/1996-1073/16/3/1163/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:16:y:2023:i:3:p:1163-:d:1042708

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().