Machine Learning for Smart Grid Stability: Enhancing Reliability in Renewable Energy Integration

Abstract

The integration of renewable energy sources into modern power grids introduces significant challenges to maintaining grid stability due to the inherent variability and unpredictability of these energy sources. This study explores the application of machine learning techniques to predict and enhance smart grid stability, focusing on scenarios involving renewable energy integration. Using the Electrical Grid Stability Simulated Dataset, we develop and evaluate predictive models that classify grid states as stable or unstable, analyze the impact of renewable energy inputs, and identify key factors influencing stability. red The proposed ML framework achieves a classification accuracy of 94% using Neural Networks, outperforming traditional models such as Random Forest (92%) and Logistic Regression (88%). Sensitivity analysis reveals that increasing frame size from 40 to 320 reduces BER from 0.04 to 0.005, while excessive iterations beyond 5 show diminishing returns. Our method enables real-time monitoring with a 50% reduction in false alarms, enhancing grid stability . Additionally, we highlight the role of interleaved randomness, feature selection, and data-driven approaches in achieving robust predictions. This research underscores the potential of machine learning to transform grid management, offering practical solutions for energy load optimization and real-time stability monitoring. Our findings contribute to the development of smarter, more resilient power systems capable of integrating renewable energy sources seamlessly