Optimized Power Distribution using Machine Learning for Load Forecasting, Fault Detection, and Voltage Regulation

Abstract

The optimization of electric power distribution systems is crucial for enhancing efficiency, reliability, and sustainability in modern power networks. Traditional optimization methods often struggle to handle the complexity and variability of large-scale power distribution grids. With the advent of ML, new opportunities have emerged to address these challenges more effectively. This paper explores the application of machine learning algorithms in optimizing power distribution systems, focusing on load forecasting, fault detection, voltage regulation, and network reconfiguration. By employing supervised, unsupervised, and reinforcement learning techniques, ML models can process vast amounts of real-time data, identify patterns, and make accurate predictions for system performance enhancement. This study presents a comprehensive review of recent advancements in ML-based optimization techniques, emphasizing their ability to improve the accuracy of load demand forecasts and reduce energy losses. Moreover, it discusses the integration of smart grid technology with ML models to enable adaptive control strategies that can respond to dynamic power demands. Various case studies and simulation results are included to demonstrate the practical benefits of machine learning applications in electric power distribution. The findings suggest that incorporating machine learning into the power distribution framework can significantly boost operational efficiency, reduce downtime, and facilitate the transition to a more intelligent and sustainable power grid. This paper concludes with a discussion of the challenges and future prospects of ML in electrical grid optimization, such as scalability, data privacy, and the need for real-time computation.