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Attention based Bi-LSTM for Power Line Partial Discharge Fault Detection

Published: 21 April 2022 Publication History

Abstract

Partial discharge is one of the faults in power distribution networks, especially in overhead lines with covered conductor of the medium voltage distribution networks, which may damage equipment and stop it’s functioning entirely. However, it is more difficult to detect partial discharge faults because of the different physical and chemical reactions and the same characterization caused by partial discharge faults, and the noise interference of the environment itself. By deploying distributed high-frequency sampling sensors to collecting three-phase voltage signal and using machine learning algorithms to detect the presence of partial discharge fault, can not only not interfere with the normal work of transmission lines to realize on-line detection of partial discharge fault, also can pass the upgrade in fault detection fault detection algorithm which can improve accuracy, therefore, it has become one of the main methods of partial discharge fault detection. In this paper, an attention mechanism based bidirectional long short term memory (Attention-LSTM) fault detection model is proposed, which can take full advantage of the bidirectional long neural network (Bi-LSTM) and attention mechanism, hence, no feature engineering expert is needed to solve the power line partial discharge fault detection issues. Experimental results show that the proposed model outperforms the existing methods in all selected performance metrics.

References

[1]
Kunjin Chen, Tomáš Vantuch, Yu Zhang, Jun Hu, and Jinliang He. 2020. Fault Detection for Covered Conductors With High-Frequency Voltage Signals: From Local Patterns to Global Features. IEEE Transactions on Smart Grid(2020).
[2]
Ming Dong and Jessie Sun. 2020. Partial discharge detection on aerial covered conductors using time-series decomposition and long short-term memory network. Electric Power Systems Research 184 (2020), 106318. https://doi.org/10.1016/j.epsr.2020.106318
[3]
L Hao and PL Lewin. 2010. Partial discharge source discrimination using a support vector machine. IEEE Transactions on Dielectrics and electrical Insulation 17, 1(2010), 189–197.
[4]
Michal Krátký, Stanislav Mišák, Petr Gajdoš, Petr Lukáš, Radim Bača, and Peter Chovanec. 2018. A Novel Method for Detection of Covered Conductor Faults in Medium Voltage Overhead Line Systems. IEEE Transactions on Industrial Electronics 65, 1 (2018), 543–552. https://doi.org/10.1109/TIE.2017.2716861
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Liping Li, Ju Tang, and Yilu Liu. 2015. Partial discharge recognition in gas insulated switchgear based on multi-information fusion. IEEE Transactions on Dielectrics and Electrical Insulation 22, 2(2015), 1080–1087.
[6]
Stanislav Mišàk, Jan Fulneček, Tomáš Vantuch, and Lukáš Prokop. 2019. Towards the character and challenges of partial discharge pattern data measured on medium voltage overhead lines. In 2019 20th International Scientific Conference on Electric Power Engineering (EPE). IEEE, 1–4.
[7]
Na Qu, Zhongzhi Li, Jiankai Zuo, and Jiatong Chen. 2020. Fault detection on insulated overhead conductors based on DWT-LSTM and partial discharge. IEEE Access 8(2020), 87060–87070.
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Wong Jee Keen Raymond, Hazlee Azil Illias, Hazlie Mokhlis, 2015. Partial discharge classifications: Review of recent progress. Measurement 68(2015), 164–181.
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Tomas Vantuch, Jan Gaura, Stanislav Misak, and Ivan Zelinka. 2016. A complex network based classification of covered conductors faults detection. In The Euro-China Conference on Intelligent Data Analysis and Applications. Springer, 278–286.

Cited By

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  • (2024)Diaspora: Resilience-Enabling Services for Real-Time Distributed Workflows2024 IEEE 20th International Conference on e-Science (e-Science)10.1109/e-Science62913.2024.10678669(1-9)Online publication date: 16-Sep-2024
  • (2024)Deep learning and data augmentation for partial discharge detection in electrical machinesEngineering Applications of Artificial Intelligence10.1016/j.engappai.2024.108074133:PAOnline publication date: 1-Jul-2024
  1. Attention based Bi-LSTM for Power Line Partial Discharge Fault Detection

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    cover image ACM Other conferences
    EEET 2021: 2021 4th International Conference on Electronics and Electrical Engineering Technology
    December 2021
    290 pages
    ISBN:9781450385169
    DOI:10.1145/3508297
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    Published: 21 April 2022

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    Author Tags

    1. Attention
    2. Distribution network
    3. Fault detection
    4. Long short term memory
    5. Medium Voltage
    6. Partial discharges

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    • Refereed limited

    Funding Sources

    • National Natural Science Key Fund
    • Science and Technology Project of NARI

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    EEET 2021

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    • (2024)Diaspora: Resilience-Enabling Services for Real-Time Distributed Workflows2024 IEEE 20th International Conference on e-Science (e-Science)10.1109/e-Science62913.2024.10678669(1-9)Online publication date: 16-Sep-2024
    • (2024)Deep learning and data augmentation for partial discharge detection in electrical machinesEngineering Applications of Artificial Intelligence10.1016/j.engappai.2024.108074133:PAOnline publication date: 1-Jul-2024

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