Design of intelligent condition monitoring and risk diagnosis system for distribution network equipment based on edge computing

Aiming at the problems of traditional distribution network monitoring system, such as lack of real-time performance, bandwidth pressure and early warning lag, this paper proposes an intelligent monitoring and risk diagnosis system for distribution network equipment based on edge computing. The system adopts a three-tier architecture of terminal perception-edge reasoning-cloud collaboration, and realizes high-frequency acquisition of electrical, mechanical, thermal and other multi-source data through Internet of Things sensors. In the edge layer, the attention mechanism is innovatively introduced to realize the adaptive fusion of multimodal data, and a lightweight Mobilene ETV 3-YOLO model is designed. By combining FP16 quantization and TensorRT optimization, the reasoning delay is reduced to 65ms, which significantly improves the real-time diagnosis ability of the edge. In the cloud, a dynamic risk assessment framework is built through Hidden Markov Model (HMM) and digital twinning technology to realize the early warning of equipment degradation more than 2 hours in advance. The experiment is based on the 12-month data of the real distribution network. The system is significantly superior to the traditional cloud solution in terms of end-to-end response delay (195ms), bandwidth consumption (0.28TB per month), fault recall rate (94.8%) and early warning time window (> 150 minutes), and the false alarm rate is reduced by 85%. The research results provide an efficient and reliable edge computing solution for intelligent operation and maintenance of distribution network.