Iterative Strategy and Quasi Real Time Processing Technology for Line Loss Analysis in Power Substations Based on Edge Intelligence

Amidst the power grid corporation's proactive alignment with the national mandate for energy transition, striving towards establishing a pioneering Chinese-style energy Internet entity on the global stage, the cornerstone of this endeavor lies in the enhancement of equipment operational surveillance online and the intricate optimization of line loss administration. Amid growing grid dimensions and escalating electrical demands, conventional line loss management strategies prove inadequate for achieving the requisite precision and efficiency. Consequently, the recurring instances of irregular line losses within substations have emerged as a pressing concern demanding prompt resolution. Therefore, this article innovatively proposes an automatic detection system for line loss anomalies in power substations based on edge intelligence technology. The system deeply integrates advanced algorithms such as deep learning (DL), and realizes real-time data processing and analysis through edge computing, which effectively shortens the data transmission delay and improves the sensitivity and accuracy of anomaly detection. The experimental results show that the system can quickly capture subtle changes in line losses in the substation area, identify and warn potential abnormal conditions in a timely manner, and provide strong technical support for power grid operation and maintenance personnel.