Machine learning approaches to predict the stability of smart grid

The smart grid is a modern solution for generating, distributing, and using energy effectively. Ensuring the stability of the smart grid is critical to guarantee safe and consistent operation. This study proposes a machine learning model to enhance the efficiency of identifying smart grid (4-node star network) stability. To accomplish this, we collected the electrical grid stability dataset from a public machine learning repository and analyzed the individual features. Different feature transformation and selection techniques were employed to generate normalized data and a subsequent feature set. Then, various classification techniques were applied to the primary and its transformations to identify the best machine-learning model for predicting smart grid stability more efficiently. The results indicated that MLP achieved the highest accuracy (97.30%) on the Z-score-transformed feature subset without optimization. This study demonstrates the potential of machine learning to accurately and efficiently predict smart grid stability, providing a critical tool to enhance the reliability, efficiency, and sustainability of the energy industry.