Intelligent Grid Optimization and Fault Prediction Based on Machine Learning

Abstract

With the intelligent development of power systems, the demand for intelligent grids in energy management, fault detection, and prediction is continuously increasing. Traditional optimization techniques and fault prediction methods are inadequate for the efficient operation of modern power grids due to their limitations. This paper explores intelligent grid optimization and fault prediction methods based on machine learning. By analyzing the shortcomings of current intelligent grid optimization technologies and fault prediction methods, it elucidates the application advantages of machine learning in grid optimization and fault prediction and provides a detailed introduction to relevant algorithms and their implementation processes. The research results show that machine learning technology has significant advantages in improving grid optimization efficiency and fault prediction accuracy, providing new solutions for the stable operation of intelligent grids.