Electrical Power System Network Security Situational Awareness and Protection Based on Deep Learning

As the electrical power system (EPS) evolves towards a comprehensive energy system, coupled with increasingly complex network attack methods, the difficulty of power information security protection has significantly increased. Deploying network attack detection systems has become a key measure to effectively address this intensifying challenge. The system has the ability to monitor the real-time operation status of EPS, quickly identify and issue warnings of potential network security risks, and help the operation and maintenance team quickly identify and repair system vulnerabilities. In this context, deep learning (DL), as a cutting-edge technology in the field of machine learning (ML), has shown extraordinary prospects in the transient security assessment of smart grids with its excellent data processing and analysis performance. This article introduces an EPS network security situational awareness and defense system that utilizes DL technology. This system utilizes DL models to deeply mine EPS massive data, aiming to accurately identify and predict network attack behavior. Experimental verification shows that the system significantly enhances the defense effectiveness of EPS against network attacks, greatly reduces the damage caused by attacks, and lays a solid foundation for the safe and efficient operation of EPS.