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XGBoost-Based Intelligent Decision Making of HVDC System with Knowledge Graph

Author

Listed:
  • Qiang Li

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

  • Qian Chen

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

  • Jiyang Wu

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

  • Youqiang Qiu

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

  • Changhong Zhang

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

  • Yilong Huang

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

  • Jianbao Guo

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

  • Bo Yang

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

Abstract

This study aims to achieve intelligent decision making in HVDC systems in the framework of knowledge graphs (KGs). First, the whole life cycle KG of an HVDC system was established by combining intelligent decision making. Then, fault diagnosis was studied as a typical case study, and an intelligent decision-making method for HVDC systems based on XGBoost that significantly improved the speed, accuracy, and robustness of fault diagnosis was designed. It is noteworthy that the dataset used in this study was extracted in the framework of KGs, and the intelligent decision making of KG and HVDC systems was accordingly combined. Four kinds of fault data extracted from KGs were firstly preprocessed, and their features were simultaneously trained. Then, sensitive weights were set, and the pre-computed sample weights were put into the XGBoost model for training. Finally, the trained test set was substituted into the XGBoost classification model after training to obtain the classification results, and the recognition accuracy was calculated by means of a comparison with the standard labels. To further verify the effectiveness of the proposed method, back propagation (BP) neural network, probabilistic neural network (PNN), and classification tree were adopted for validation on the same fault dataset. The experimental results show that the XGBoost used in this paper could achieve accuracy of over 87% in multiple groups of tests, with recognition accuracy and robustness being higher than those of its competitors. Therefore, the method proposed in this paper can effectively identify and diagnose faults in HVDC systems under different operation conditions.

Suggested Citation

  • 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.
  • Handle: RePEc:gam:jeners:v:16:y:2023:i:5:p:2405-:d:1086098
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    References listed on IDEAS

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    1. Yang, Bo & Wu, Shaocong & Li, Qiang & Yan, Yingjie & Li, Danyang & Luo, Enbo & Zeng, Chunyuan & Chen, Yijun & Guo, Zhengxun & Shu, Hongchun & Li, Zilin & Wang, Jingbo, 2023. "Jellyfish search algorithm based optimal thermoelectric generation array reconfiguration under non-uniform temperature distribution condition," Renewable Energy, Elsevier, vol. 204(C), pages 197-217.
    2. Amr S. Zalhaf & Ensheng Zhao & Yang Han & Ping Yang & Abdulrazak H. Almaliki & Reda M. H. Aly, 2022. "Evaluation of the Transient Overvoltages of HVDC Transmission Lines Caused by Lightning Strikes," Energies, MDPI, vol. 15(4), pages 1-20, February.
    3. 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.
    4. Yang, Bo & Wu, Shaocong & Zhang, Hao & Liu, Bingqiang & Shu, Hongchun & Shan, Jieshan & Ren, Yaxing & Yao, Wei, 2022. "Wave energy converter array layout optimization: A critical and comprehensive overview," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).
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    Cited by:

    1. 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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