Design and Implementation of AI-based Hydropower Station Monitoring System

This paper studies the intelligent hydropower station monitoring system, focusing on the application of AI system in it. The system adopts a hierarchical distributed architecture. The data acquisition layer uses a variety of sensors to collect the operating parameters and environmental data of key equipment of the hydropower station at high frequency and high precision; the network transmission layer builds a reliable transmission network based on the environmental characteristics of the hydropower station to ensure real-time and stable data transmission; the monitoring center layer realizes centralized data processing and visualization display, which is convenient for operation and maintenance personnel to monitor and make decisions. Then an improved spatiotemporal fusion neural network (ISTFN) algorithm is proposed, which effectively integrates time series and spatial distribution data through a unique spatiotemporal fusion mechanism. The performance of this algorithm is verified by experiments with historical data and real time data from an actual hydropower station. Intel Xeon Platinum 8380 CPU and NVIDIA A100 GPU are used in the experiment. Experimental results show that ISTFN algorithm achieves 92.6% fault prediction accuracy, higher by 15.3% than conventional LSTM algorithm by 15.3%; and ISTFN algorithm reaches 89.5% on F1 score than traditional algorithm. These data fully demonstrate that the ISTFN algorithm has significantly improved the performance of the intelligent hydropower station monitoring system and provided strong support for the intelligent development of hydropower stations.