Research on Real-time Monitoring and Fault Early Warning Algorithm of Substation Equipment Based on Internet of Things

This paper studies the real-time monitoring and fault early warning algorithm of substation equipment based on Internet of Things (IoT) technology, aiming at improving the stability and security of power system. Aiming at the problems of low efficiency and untimely early warning in traditional substation equipment condition monitoring methods, this paper designs and implements an efficient and accurate real-time monitoring and fault early warning system. The system adopts layered architecture, including sensing layer, transmission layer, processing layer and application layer. It collects key data such as electrical parameters, temperature and humidity of substation equipment in real time through various sensors, and uses wireless communication and wired communication technologies to ensure efficient data transmission. In the processing layer, the system uses cloud computing platform to store, process and analyze the received data, and introduces ensemble learning algorithm, combining the advantages of support vector machine (SVM) and neural network, and constructs a powerful fault early warning model. The experimental results show that the system can accurately monitor the equipment status and send out early warning signals when potential faults are found, which effectively improves the accuracy and real-time performance of fault early warning. This study provides a strong support for the intelligent upgrading of the power industry and has a wide application prospect.