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

Yu Zhang, Jinyue Shi, Shicheng Huang, Liang Geng, Zexiong Wang, Hao Sun, Qingguang Yu (), Ding Liu, Xin Yao, Weihua Zuo, Min Guo and Xiaoyu Che
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Yu Zhang: Shijiazhuang Power Supply Company, State Grid Hebei Electric Power Co., Ltd., Shijiazhuang 050000, China
Jinyue Shi: Shijiazhuang Power Supply Company, State Grid Hebei Electric Power Co., Ltd., Shijiazhuang 050000, China
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
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

Energies, 2025, vol. 18, issue 20, 1-16

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.

Keywords: reliability assessment; random forests model; SHAP values; weak link identification (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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