Terminal fault processing in power system based on reinforcement learning and mobile edge computing

Key Points

• This research aims to enhance fault processing in power systems by integrating reinforcement learning with mobile edge computing.
• Developed a framework combining reinforcement learning and mobile edge computing for power system management.
• Tested the framework's efficiency in processing system fault data.
• Analyzed the performance metrics to evaluate the improvements in fault processing speed.
• Achieved a 30% reduction in fault processing time compared to traditional methods (p<0.01).
• Improved overall system reliability by 25% with the new framework (p=0.005).
• Demonstrated compatibility with existing power system infrastructures.