Research on remote sensing image recognition and intelligent decision support system for unplanned power grid operations

This paper introduces a cutting-edge solution for enhancing the supervision of power grid operations, integrating remote sensing, AI, and advanced algorithms to ensure grid safety and efficiency. Leveraging deep learning, the system accurately interprets remote sensing images to detect unauthorized activities and assess safety compliance with over 95% accuracy. A comprehensive risk assessment model, considering environmental, load, and weather factors, enables precise risk level determination and early warnings. Additionally, an innovative decision optimization engine, utilizing evolutionary algorithms and reinforcement learning, facilitates the balanced optimization of operational costs and efficiency within risk boundaries. Systematic evaluation demonstrates the solution's capability to significantly boost operational control efficiency and reduce risks, highlighting its promising application potential in power grid management.