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SHAP-Enhanced Artificial Intelligence Machine Learning Framework for Data-Driven Weak Link Identification in Regional Distribution Grid Power Supply Reliability

Author

Listed:
  • Yu Zhang

    (Shijiazhuang Power Supply Company, State Grid Hebei Electric Power Co., Ltd., Shijiazhuang 050000, China
    These authors contributed equally to this work.)

  • Jinyue Shi

    (Shijiazhuang Power Supply Company, State Grid Hebei Electric Power Co., Ltd., Shijiazhuang 050000, China
    These authors contributed equally to this work.)

  • Shicheng Huang

    (Shijiazhuang Power Supply Company, State Grid Hebei Electric Power Co., Ltd., Shijiazhuang 050000, China)

  • Liang Geng

    (Shijiazhuang Power Supply Company, State Grid Hebei Electric Power Co., Ltd., Shijiazhuang 050000, China)

  • Zexiong Wang

    (Shijiazhuang Power Supply Company, State Grid Hebei Electric Power Co., Ltd., Shijiazhuang 050000, China)

  • Hao Sun

    (Shijiazhuang Power Supply Company, State Grid Hebei Electric Power Co., Ltd., Shijiazhuang 050000, China)

  • Qingguang Yu

    (Department of Electrical Engineering, Tsinghua University, Beijing 100084, China)

  • Ding Liu

    (Department of Electrical Engineering, Tsinghua University, Beijing 100084, China
    These authors contributed equally to this work.)

  • Xin Yao

    (Department of Electrical Engineering, Tsinghua University, Beijing 100084, China)

  • Weihua Zuo

    (Department of Electrical Engineering, Tsinghua University, Beijing 100084, China)

  • Min Guo

    (Department of Electrical Engineering, Tsinghua University, Beijing 100084, China)

  • Xiaoyu Che

    (Department of Electrical Engineering, Tsinghua University, Beijing 100084, China)

Abstract

Reliability assessment of power systems is essential for ensuring the secure and stable operation of power grids, and identifying weak links constitutes a critical step in enhancing system reliability. Traditional deterministic methods are limited in their ability to capture the complex, nonlinear relationships between component failures and overall system risk. To overcome this limitation, this paper proposes an explainable machine learning-based approach for identifying weak components in power systems. Specifically, a set of contingency scenarios is constructed through enumeration, and a random forest regression model is trained to map transmission line outage events to the amount of system load curtailment. The trained model is then interpreted using SHapley Additive exPlanations (SHAP) values. By aggregating these values, the global reliability contribution of each component is quantified. The proposed method is validated on the IEEE 57-bus system, and the results demonstrate its effectiveness and feasibility. This research offers a data-driven framework for translating system-level reliability metrics into device-level quantitative attributions, thereby enabling interpretable identification of weak links.

Suggested Citation

  • Yu Zhang & Jinyue Shi & Shicheng Huang & Liang Geng & Zexiong Wang & Hao Sun & Qingguang Yu & Ding Liu & Xin Yao & Weihua Zuo & Min Guo & Xiaoyu Che, 2025. "SHAP-Enhanced Artificial Intelligence Machine Learning Framework for Data-Driven Weak Link Identification in Regional Distribution Grid Power Supply Reliability," Energies, MDPI, vol. 18(20), pages 1-16, October.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:20:p:5372-:d:1769499
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    References listed on IDEAS

    as
    1. Jesus Beyza & Eduardo Garcia-Paricio & Jose M. Yusta, 2019. "Applying Complex Network Theory to the Vulnerability Assessment of Interdependent Energy Infrastructures," Energies, MDPI, vol. 12(3), pages 1-16, January.
    2. Liu, Jun & Xiong, Qingyu & Shi, Weiren & Shi, Xin & Wang, Kai, 2016. "Evaluating the importance of nodes in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 452(C), pages 209-219.
    3. Azzolin, Alberto & Dueñas-Osorio, Leonardo & Cadini, Francesco & Zio, Enrico, 2018. "Electrical and topological drivers of the cascading failure dynamics in power transmission networks," Reliability Engineering and System Safety, Elsevier, vol. 175(C), pages 196-206.
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