Collaborative Error Prevention Strategy and Algorithm for Main Distribution in Electric Power System

As the most core working link in the Electric Power System (EPS), the technical level of power dispatching directly affects the stability and safety of power supply. To ensure the safe and stable operation of the power grid, EPS must rely on a scientific and effective dispatch information management system, and fully utilize intelligent error prevention technology to achieve intelligent control of power grid dispatch and operation, thereby minimizing the occurrence of misoperations. In response to the challenges in the current EPS scheduling operation, this article deeply explores the main distribution collaborative error prevention strategy, aiming to optimize the scheduling strategy and improve algorithm performance, achieve collaborative operation between the main network and the distribution network, and improve the overall safety level of the power grid. On this basis, this article proposes an intelligent scheduling and monitoring system based on deep learning (DL). The system utilizes DL algorithm for deep mining and analysis of power grid operation data, and monitors the real-time operation status of the power grid. The experimental results show that the intelligent scheduling and monitoring system performs well in improving the safety and stability of power grid operation, significantly reducing the risk of misoperation and providing strong technical support for the safe and stable operation of EPS.