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A Decentralized Fault Section Location Method Using Autoencoder and Feature Fusion in Resonant Grounding Distribution Systems

Li, Zi Jing; Lin, Shuyue; Guo, Mou Fa; Tang, J.

Authors

Zi Jing Li

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Dr Shuyue Lin S.Lin@hull.ac.uk
Lecturer in Electrical and Electronic Engineering

Mou Fa Guo

J. Tang



Abstract

In industrial applications, the existing fault location methods of resonant grounding distribution systems suffer from low accuracy due to excessive dependence on communication, lack of field data, difficulty in artificial feature extraction and threshold setting, etc. To address these problems, this study proposes a decentralized fault section location method, which is implemented by the primary and secondary fusion intelligent switch (PSFIS) with two preloaded algorithms: autoencoder (AE) and backpropagation neural network. The relation between the transient zero-sequence current and the derivative of the transient zero-sequence voltage in each section is analyzed, and its features are extracted adaptively by using AE, without acquiring network parameters or setting thresholds. The current and voltage data are processed locally at PSFISs throughout the whole procedure, making it is insusceptible to communication failure or delay. The feasibility and effectiveness of the approach are investigated in PSCAD/EMTDC and real-time digital simulation system, which is then validated by field data. Compared with other methods, the experiment results indicate that the proposed method performs well in various scenarios with strong robustness to harsh on-site environment and roughness of data.

Citation

Li, Z. J., Lin, S., Guo, M. F., & Tang, J. (2022). A Decentralized Fault Section Location Method Using Autoencoder and Feature Fusion in Resonant Grounding Distribution Systems. IEEE systems journal, https://doi.org/10.1109/JSYST.2022.3151630

Journal Article Type Article
Acceptance Date Feb 7, 2022
Online Publication Date Mar 4, 2022
Publication Date 2022
Deposit Date Apr 29, 2022
Publicly Available Date May 12, 2022
Journal IEEE Systems Journal
Print ISSN 1932-8184
Electronic ISSN 1937-9234
Publisher Institute of Electrical and Electronics Engineers
Peer Reviewed Peer Reviewed
DOI https://doi.org/10.1109/JSYST.2022.3151630
Keywords Autoencoder (AE); Backpropagation neural network; Fault section location; Resonant grounding (RG) distribution systems
Public URL https://hull-repository.worktribe.com/output/3983665

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Accepted manuscript (980 Kb)
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