Publication

Abstract : This paper explores how machine learning can improve power system protection by predicting critical parameters such as inertia constants and frequency deviations. In the current era, various power systems are consistently integrating renewable energy sources, hence, maintaining the stability of the system has become complex. This study analyzes two configurations i.e. a conventional grid and a microgrid. Machine Learning models are applied to predict frequency deviations and inertia variations under different operating conditions. By forecasting system responses to disturbances, the proposed approach aims to provide timely insights into potential frequency shifts and stability needs. This predictive framework aims to equip power systems with greater resilience and adaptability, addressing the challenges posed by modern grid demands and the variable nature of renewable energy sources.