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Classifying and predicting the stability of a decentralized 4-node star smart grid system is explored in this paper using machine learning and deep learning models such as LSTM, GRU, CNN, MLP, and TabNet. Advanced models such as TabNet (97.4% accuracy) and MLP (96.8% performance) perform better than more conventional algorithms, according to the results. These results demonstrate how intelligent models can help make grids more resilient and how they can back up the implementation of stronger decentralized smart grid control strategies in commercial and manufacturing environments.

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Enhancing Electrical Grid Stability: A Local Stability Analysis of the 4-Node Star System Using Decentralized Smart Grid Control

  • 会议论文

  • 发表日期：

    2026年

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  • Ziani Said,
  • Achmad Rizal

摘要

Classifying and predicting the stability of a decentralized 4-node star smart grid system is explored in this paper using machine learning and deep learning models such as LSTM, GRU, CNN, MLP, and TabNet. Advanced models such as TabNet (97.4% accuracy) and MLP (96.8% performance) perform better than more conventional algorithms, according to the results. These results demonstrate how intelligent models can help make grids more resilient and how they can back up the implementation of stronger decentralized smart grid control strategies in commercial and manufacturing environments.