抄録
With the deepening digital transformation of power systems and the integration of high proportions of renewable energy, the operational environment of power grids has become increasingly complex, posing unprecedented challenges to safety and reliability. Traditional safety risk assessment and early warning methods have limitations in addressing dynamic system changes and nonlinear characteristics, whereas the application of big data and artificial intelligence (AI) technologies provides new approaches and tools for intelligent safety management of power systems. This paper systematically reviews the current state of research on safety risk assessment in power systems, focusing on big data-driven risk quantification methods and AI-enhanced fault prediction mechanisms. It proposes a design framework and implementation path for intelligent early warning systems. Through practical case studies, this paper analyzes typical application scenarios of these technologies, such as equipment health monitoring, extreme weather forecasting, and cybersecurity protection. Finally, the paper summarizes key challenges in smart grid development, including data privacy protection, model interpretability, and limited real-time responsiveness, and outlines future research directions. By exploring the integrated application of big data and AI technologies in power system safety risk assessment, this paper aims to provide theoretical foundations and technical support to enhance the safety and reliability of smart grids, contributing to stable operation and sustainable development of power systems.
