Research And Implementation Of Smart Grid Monitoring System Based On Edge Cloud

The power grid is the cornerstone of the country,and the safe and stable operation of the power grid is of great significance to promoting social development and ensuring national security.With the development of social economy,the digital society has put forward stricter requirements on the reliability of the power grid system,and the demand for intelligent transformation of Chinese traditional power grid is becoming more and more urgent.In order to detect abnormal grid operation and power equipment failures in time,it is necessary to monitor the status data of each link of the grid in real time.The related research of smart grid status detection system has become a hot research topic in the construction of smart grid.This paper designs and implements a condition monitoring system based on edge-cloud collaboration in the industrial application scenario of the smart grid.Based on condition monitoring technology and anomaly detection theory,the system monitors key data of power system operation in real time,and realizes intelligent anomaly detection of state data at the same time.The main research content includes the following aspects:(1)Designed and implemented a smart grid status monitoring system based on publishsubscribe pattern.Aiming at the problems of poor real-time performance,strong coupling,and poor scalability of traditional condition monitoring systems,the microservice architecture is used for system design.Choose the Message Queue Telemetry Transport(MQTT)protocol based on the publish-subscribe pattern as the communication protocol of the system.After that,functional modules such as data acquisition,transmission,storage,and visualization were designed in turn to realize the basic functions of the condition monitoring system,while improving the responsiveness and scalability of the system,optimizing system performance.(2)Research on anomaly detection algorithms for smart grid scenarios.Aiming at the problem that traditional power grid equipment anomaly detection relies on manual regular maintenance,and the power data collected by the system in(1)is not fully utilized,three types of anomaly detection algorithms are experimented: reconstruction-based algorithms,prediction-based algorithms,and spectral residual algorithms.And through experiments on various data sets such as the UK-DALE dataset,the VSB transmission line fault detection dataset,and the PJM hourly power consumption dataset,the effectiveness of various anomaly detection algorithms in power grid application scenarios is verified.(3)Designed and implemented a smart grid anomaly detection module with edge-cloud collaboration.First,based on the edge-cloud collaboration,integrated with the anomaly detection algorithm studied in(2),the anomaly detection module structure is designed.Then,according to the characteristics of the cloud and the edge,the cloud anomaly detection module and the edge anomaly detection module are designed in turn,and the algorithm and sub-module structure are further optimized for the application of the anomaly detection algorithm at the edge.Finally,the module is applied to the system in(1)to realize the abnormal monitoring function of the system.