Decentralized Smart Grid Stability Evaluation using ML and DL Approaches

The growing integration of decentralized energy technologies such as distributed generation units, microgrids, and systems with bidirectional power flow has significantly complicated the task of maintaining grid stability. Conventional centralized control strategies often fall short when addressing the localized, rapidly changing dynamics of decentralized electrical networks. This paper explores the use of traditional machine learning technics and advanced deep learning models namely Random Forest (RF), Support Vector Machine (SVM), and Convolutional Neural Networks (CNN) to forecast stability conditions in such distributed systems. A dataset of UCIs from kaggle repository is taken into consideration of simulated measurements like tau[x], p[x], g[x], stab, stabf, and node-based interactions, is employed for model development and evaluation. The RF algorithm proves effective in handling diverse input features and offers interpretable results achieving a peak prediction accuracy of 98.95%, while the SVM model excels at classifying distinct stability states with an accuracy of 96.75%, whereas CNNs classifies confusion matrix with 81% accuracy. The findings demonstrate that decentralized deployment of intelligent, data-driven models can enable autonomous decision-making and enhance system robustness. Overall, this study highlights machine learning as a promising tool for ensuring stability in next-generation decentralized power infrastructures.