Design of a Real-time Line Loss Diagnosis System for Distribution Area Based on Edge Computing in the Power Internet of Things

This paper designs and implements a real-time line loss diagnosis system for distribution area based on edge computing in the Power Internet of Things, aiming to address the problems of poor real-time performance and low accuracy in traditional line loss diagnosis methods. The system adopts a three-tier architecture of "end–edge–cloud", which is responsible for data acquisition, real-time processing, and advanced analysis, respectively. Smart meters and sensors are used to collect power data in real time, while edge computing nodes integrate data cleaning, data compression, and machine learning algorithms for localized processing and real-time diagnosis. Meanwhile, the cloud platform performs global analysis and optimization. Experimental results show that the proposed system significantly outperforms traditional methods in terms of real-time performance, accuracy, and stability, providing a new solution for the efficient operation of power distribution systems.