Application of Spatiotemporal Data Prediction Method in Intelligent Monitoring System for Early Warning of Equipment Failure in Power Distribution Room

Timely warning of equipment failure in the distribution room is crucial to the safe operation of the power system, but traditional monitoring methods have problems with prediction delay and poor accuracy, which leads to the inability to detect potential failures in advance and thus effectively avoid the losses caused by failures. To solve these problems, this paper proposes an intelligent monitoring system based on spatiotemporal data prediction method, which aims to achieve early warning of equipment failure in the distribution room. First, by deeply analyzing the spatiotemporal data of the equipment, a suitable spatiotemporal sequence model is selected to establish a model of the equipment operation status. Then, this paper introduces the spatiotemporal convolutional network (STCN) algorithm to predict equipment failures. By combining with real-time monitoring data, the system can issue early warnings before the equipment becomes abnormal, effectively reducing the impact of equipment failures on the power system. Experimental results show that the STCN algorithm performs well in fault prediction accuracy, with an accuracy rate that is stable between 0.89 and 0.94. In terms of early warning time, STCN can issue a warning 10 to 15 minutes before a fault occurs, which is significantly better than LSTM (Long Short-Term Memory) and SVM (Support Vector Machine). The STCN algorithm is also better than LSTM and SVM in terms of response speed. From the above data, it can be seen that the STCN algorithm is effective in early warning of equipment failures in distribution rooms, providing an efficient and reliable technical solution for intelligent monitoring of power systems.