Theoretical Research Review on Artificial Intelligence-Enhanced Power System Dynamic Resilience

Power systems face unprecedented complex challenges against the dual backdrop of accelerating energy transition and intensifying global climate change. To achieve "dual carbon" goals, large-scale integration of intermittent and volatile renewables like wind and solar is essential, increasing operational and dynamic stability challenges for power systems. Simultaneously, more frequent and intense extreme weather events heighten risks to power infrastructure and raise the likelihood of large-scale blackouts. The combined effect of these two factors drastically increases the risk of power systems suffering complex, variable, and highly dynamic disturbance impacts, posing severe challenges to their safe and stable operation. Traditional resilience enhancement methods have real-time performance and adaptability limitations, while artificial intelligence technology provides a new paradigm for building a dynamic resilience enhancement system. This paper systematically reviews the application of AI in power system dynamic resilience, focusing on analyzing AI-enabled dynamic resilience frameworks, key technologies, and development pathways. Comparing traditional methods reveals the advantages of AI technology in improving power system stability, enhancing scheduling decision accuracy, and reducing operation and maintenance costs. Simultaneously, it discusses current technical bottlenecks and future research directions, providing theoretical references for constructing a new generation of resilient power grids.