Abstract
With the increasing scale and complexity of power systems, traditional state monitoring and fault diagnosis methods can no longer meet the efficiency and reliability demands of modern power grids. In recent years, the rapid development of big data and artificial intelligence (AI) technologies has provided new opportunities for intelligent monitoring and diagnosis in power systems. This paper systematically examines the key applications of big data technology in power system state monitoring, covering data acquisition, processing, and analysis methods. It explores big data-based fault prediction models in detail. Meanwhile, it provides an in-depth analysis of AI applications in power system fault diagnosis, highlighting the role of machine learning and deep learning algorithms in enhancing fault identification accuracy and prediction precision. Through practical case studies, this paper showcases the successful applications of big data and AI in power systems, discussing the technical challenges and corresponding solutions in their implementation. Furthermore, it presents future development trends in state monitoring and fault diagnosis, emphasizing potential research hotspots and technological breakthroughs. This paper aims to serve as a comprehensive reference for academic researchers and engineering practitioners, contributing to the continuous advancement of power system intelligence.
