@article{Parginos2026, 
author = {Konstantinos Parginos and Marios Tyrovolas and Ricardo J. Bessa and X. San Liang and Chrysostomos Stylios and Simon Camal and George Kariniotakis},
title = {Interpretable Power Grid Overload Detection with Information Flow-based Fuzzy Cognitive Maps},
year = {2026},
journal = {CSEE Journal of Power and Energy Systems},
volume = {12},
number = {4},
pages = {1668-1680},
keywords = {Causality, explainable, fuzzy cognitive maps, grid overloading, interpretability, power grid, XAI},
url = {https://www.sciopen.com/article/10.17775/CSEEJPES.2024.04780},
doi = {10.17775/CSEEJPES.2024.04780},
abstract = {The increasing integration of renewable energy sources (RESs) brings significant challenges, such as grid overloading, which can escalate operational costs and bring 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 compromise 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 demonstrate predictive performance comparable to state-of-the-art models such as Explainable Boosting Machines (EBM) and Logistic Regression (LR), while offering superior interpretability. These strengths establish IF-FCMs as a reliable and powerful tool for future energy management systems.}
}