Development of Intelligent Fault Early Warning System for Substation Integrating Satellite Remote Sensing and Ground Monitoring

In this paper, an intelligent fault early warning system of substation integrating satellite remote sensing and ground monitoring technology is proposed and developed, aiming at improving the safety and stability of substation operation. The system architecture is designed as four layers, including data acquisition layer, data processing and fusion layer, fault early warning layer and user interaction layer. The data acquisition layer is responsible for collecting satellite remote sensing images and ground monitoring data; The data processing and fusion layer preprocesses, extracts and fuses the original data to improve the data quality. Fault early warning layer uses machine learning algorithm to build fault early warning model, and automatically identifies and predicts potential faults; The user interaction layer displays the early warning information in real time through the graphical interface, and provides visual fault analysis results. The fault early warning model based on ensemble learning adopts random forest algorithm and introduces weighted voting mechanism, which improves the accuracy and stability of prediction. Case analysis shows that the system has significantly improved the accuracy and timeliness of fault early warning in practical application, from 85.234% to 96.789%. The research results show that the intelligent fault early warning system of substation integrating satellite remote sensing and ground monitoring can overcome the limitations of traditional technology, realize comprehensive, real-time monitoring and efficient early warning of substation operation state, and provide strong support for the stable operation of power system.