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Evaluation, Analysis and Diagnosis for HVDC Transmission System Faults via Knowledge Graph under New Energy Systems Construction: A Critical Review

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Listed:
  • Jiyang Wu

    (CSG EHV Power Transmission Company of China Southern Power Grid Co., Ltd., Guangzhou 510000, China)

  • Qiang Li

    (CSG EHV Power Transmission Company of China Southern Power Grid Co., Ltd., Guangzhou 510000, China)

  • Qian Chen

    (CSG EHV Power Transmission Company of China Southern Power Grid Co., Ltd., Guangzhou 510000, China)

  • Guangqiang Peng

    (Maintenance and Test Center of CSG EHV Power Transmission Company of China Southern Power Grid Co., Ltd., Guangzhou 510663, China)

  • Jinyu Wang

    (CSG EHV Power Transmission Company of China Southern Power Grid Co., Ltd., Dali Bureau, Dali 671000, China)

  • Qiang Fu

    (CSG EHV Power Transmission Company of China Southern Power Grid Co., Ltd., Dali Bureau, Dali 671000, China)

  • Bo Yang

    (Faculty of Electric Power Engineering, Kunming University of Science and Technology, Kunming 650500, China)

Abstract

High voltage direct current (HVDC) transmission systems play a critical role to optimize resource allocation and stabilize power grid operation in the current power grid thanks to their asynchronous networking and large transmission capacity. To ensure the operation reliability of the power grid and reduce the outage time, it is imperative to realize fault diagnosis of HVDC transmission systems in a short time. Based on the prior research on fault diagnosis methods of HVDC systems, this work comprehensively summarizes and analyzes the existing fault diagnosis methods from three different angles: fault type, fault influence, and fault diagnosis. Meanwhile, with the construction of the digital power grid system, the type, quantity, and complexity of power equipment have considerably increased, thus, traditional fault diagnosis methods can basically no longer meet the development needs of the new power system. Artificial intelligence (AI) techniques can effectively simplify solutions’ complexity and enhance self-learning ability, which are ideal tools to solve this problem. Therefore, this work develops a knowledge graph technology-based fault diagnosis framework for HVDC transmission systems to remedy the aforementioned drawbacks, in which the detailed principle and mechanism are introduced, as well as its technical framework for intelligent fault diagnosis decision.

Suggested Citation

  • Jiyang Wu & Qiang Li & Qian Chen & Guangqiang Peng & Jinyu Wang & Qiang Fu & Bo Yang, 2022. "Evaluation, Analysis and Diagnosis for HVDC Transmission System Faults via Knowledge Graph under New Energy Systems Construction: A Critical Review," Energies, MDPI, vol. 15(21), pages 1-20, October.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:21:p:8031-:d:956557
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    References listed on IDEAS

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    1. Pierri, Erika & Binder, Ole & Hemdan, Nasser G.A. & Kurrat, Michael, 2017. "Challenges and opportunities for a European HVDC grid," Renewable and Sustainable Energy Reviews, Elsevier, vol. 70(C), pages 427-456.
    2. Li, Jianwei & Yang, Qingqing & Mu, Hao & Le Blond, Simon & He, Hongwen, 2018. "A new fault detection and fault location method for multi-terminal high voltage direct current of offshore wind farm," Applied Energy, Elsevier, vol. 220(C), pages 13-20.
    3. Benasla, Mokhtar & Allaoui, Tayeb & Brahami, Mostefa & Denaï, Mouloud & Sood, Vijay K., 2018. "HVDC links between North Africa and Europe: Impacts and benefits on the dynamic performance of the European system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 3981-3991.
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    Cited by:

    1. Bing Liu & Jichong Lei & Jinsen Xie & Jianliang Zhou, 2022. "Development and Validation of a Nuclear Power Plant Fault Diagnosis System Based on Deep Learning," Energies, MDPI, vol. 15(22), pages 1-15, November.
    2. Qiang Li & Qian Chen & Jiyang Wu & Youqiang Qiu & Changhong Zhang & Yilong Huang & Jianbao Guo & Bo Yang, 2023. "XGBoost-Based Intelligent Decision Making of HVDC System with Knowledge Graph," Energies, MDPI, vol. 16(5), pages 1-21, March.
    3. Bo Yang & Yulin Li & Wei Yao & Lin Jiang & Chuanke Zhang & Chao Duan & Yaxing Ren, 2023. "Optimization and Control of New Power Systems under the Dual Carbon Goals: Key Issues, Advanced Techniques, and Perspectives," Energies, MDPI, vol. 16(9), pages 1-4, May.

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