Stability Prediction in Sustainable Energy Systems Using Machine Learning Models

In the twenty-first century, there is a rising necessity to meet growing electricity demands while shifting towards sustainable practices. This shift involves incorporating renewable energy sources into the power grid, which brings both opportunities and challenges. Smart grids, enabled by advanced connectivity and renewable technologies, offer a solution, but they also add complexity to grid management. This paper focuses on how machine learning can help predict power system stability, a critical aspect of grid management. The proposed methodology uses machine learning, specifically multi modeling, to forecast stability more accurately. By analyzing diverse datasets covering factors like demand, supply, environmental variables, and grid dynamics, machine learning models can capture complex patterns in power system behavior. The proposed approach aims to improve the reliability of stability predictions, allowing for proactive decision-making and real-time interventions. Through a systematic evaluation of different machine learning models, this paper identifies the best framework for practical use. An impressive 96% accuracy has been achieved using ANN. This research contributes to the advancement of machine learning in ensuring stable and resilient power grids, thereby supporting a sustainable energy future.