Article Dans Une Revue CSEE Journal of Power and Energy Systems Année : 2025

Interpretable Power Grid Overload Detection with Information Flow-based Fuzzy Cognitive Maps

Résumé

The increasing integration of renewable energy sources (RES) brings significant challenges, such as grid overloading, which can escalate operational costs and brings a risk of system-wide blackouts. Traditional AI-based solutions often fall short due to their "black-box" nature, while many existing eXplainable AI (XAI) methods either provide unreliable post-hoc explanations or present compromised reliability and generalizability due to their vulnerability to spurious correlations. This study introduces a novel framework based on recently introduced Information Flow-based Fuzzy Cognitive Maps (IF-FCMs), a causal eXplainable Artificial Intelligence (CXAI) approach designed to overcome these limitations. By employing a two-layered architecture, this framework not only detects grid overloads but also identifies both local and global factors causing these events. Tested on the Medium Voltage Oberrhein network, IF-FCMs demonstrated predictive performance comparable to state-of-theart models such as Explainable Boosting Machines (EBM) and Logistic Regression (LR), while offering superior interpretability. These strengths establish IF-FCMs as a reliab

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Dates et versions

hal-05110354 , version 1 (12-06-2025)

Identifiants

Citer

Konstantinos Parginos, Marios Tyrovolas, Ricardo J Bessa, X. San Liang, Chrysostomos Stylios, et al.. Interpretable Power Grid Overload Detection with Information Flow-based Fuzzy Cognitive Maps. CSEE Journal of Power and Energy Systems, 2025, pp.1-12. ⟨10.17775/CSEEJPES.2024.04780⟩. ⟨hal-05110354⟩
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