Smart Grid Stability Analysis with Interpretable Machine Learning and Deep Learning Models

Shamanta Sharmi Sristy, Iftekharul Islam, Mahmudul Hasan (), Md. Motiur Rahman Tareq () and Kanij Fatema
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Shamanta Sharmi Sristy: Hajee Mohammad Danesh Science and Technology University
Iftekharul Islam: Hajee Mohammad Danesh Science and Technology University
Mahmudul Hasan: Deakin University
Md. Motiur Rahman Tareq: Hajee Mohammad Danesh Science and Technology University
Kanij Fatema: Hajee Mohammad Danesh Science and Technology University

A chapter in Machine Learning Technologies on Energy Economics and Finance, 2025, pp 285-310 from Springer

Abstract: Abstract Ensuring the stability of modern power grids is paramount to achieving sustainable energy systems, particularly as renewable energy sources become more integrated. This study focuses on predicting smart grid stability using machine learning (ML), deep learning (DL) algorithms, and Explainable AI (XAI) methods to ensure model interpretability. Among the ML models, SVM outperformed others with an accuracy of 97.8%, while Multilayer Perceptron achieved the highest accuracy of 97.7% among DL models. Explainable AI techniques, particularly SHAP was employed to interpret model predictions, revealing key factors such power load (g3) and reaction times (tau1) significantly influence grid stability. Our approach ensures the grid’s stability by addressing fluctuations and system imbalances in real time. The study’s findings offer practical guidance for enhancing power grid performance, which advances the more general objectives of energy resilience and efficiency in the incorporation of renewable energy sources.

Keywords: Smart grid; Grid stability; Machine learning; Deep learning; Explainability (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:spr:isochp:978-3-031-95099-5_13

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DOI: 10.1007/978-3-031-95099-5_13

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