Research On Intelligent Substation Auxiliary Monitoring And Early Warning Based On The Internet Of Things

Because of its wide range of sensor nodes, ubiquitous network transmission and intelligent data processing, the Internet of Things(IOT) has been more and more used in various aspects of the Smart Grid. It has wide application space in the power system since its basic link in the perception terminal of Smart Grid. As an important foundation of the Smart Grid, smart substation can provides fully space for key technologies of IOT.Substation intelligent auxiliary control system is an important part of intelligent substation. By introducing IOT, modern communication technology, visualization technology and control technology, intelligent monitoring, data analysis, alarm linkage and visual display for the environment, dynamics and equipment hotspot are realized through all kinds of auxiliary facilities for substation. Through integrating the function modules that are closely related to the IOT in the substation intelligent auxiliary control system, intelligent substation auxiliary monitoring system is constructed.In this paper, survey and analysis on the status of substation intelligent auxiliary control system are completed. According to the overall requirement of substation intelligent auxiliary control system, IOT is introduced to construct the system. All kinds of data thiough large amount of terminal equipments constructed by IOT are collected and transmitted to the application layer for intelligent analysis and management by some networks. In this way, it improves the level of intelligent substation.To realize intelligent monitoring for some key nodes of substation equipment, this paper focuses on the application of wireless sensor network (WSN) in the substation intelligent auxiliary monitoring system. IOT based temperature monitoring platform for substation equipment is built. Low-power AVR single-chip computer, wireless transceiver module and temperature sensor are used at the sensing layer to collect and transmit temperature data of the equipment; at the network layer, Industrial Ethernet is adopted to transmit real-time data within the station; at the application layer, a prediction algorithm based on real-time temperature data is introduced. The algorithm focuses on short-term predicting method through chaotic time series prediction based on support vector machine (SVM). Through the comparison of actual and predicted values, the prediction algorithm is proved to achieve greater forecasting accuracy. According to the short-term predicted values of the temperature, early warning for the equipment hotspots is achieved.