Research on Predictive Maintenance Algorithm of Giant Hydropower Units in Smart Power Plan

The operation state of giant hydropower units is related to the stability of power supply and the efficient use of clean energy. In this article, a predictive maintenance algorithm for giant hydropower units is proposed and studied in combination with the technical characteristics of smart power plants. In the research, multi-source operation data (such as vibration, temperature, pressure, etc.) are integrated, and an algorithm framework based on adaptive learning is designed to realize real-time monitoring and accurate prediction of equipment health status. The results show that the prediction accuracy of the algorithm is 93.2 %, the recall rate is 91.5 %, and the false alarm rate and missed alarm rate are as low as 1.8 % and 3.2 % respectively. These performances are superior to traditional methods such as deep Q-network (DQN), Kalman filter (KF) and random forest (RF). The algorithm is outstanding in computational efficiency, the single prediction takes only 0.45 seconds, and the memory occupation is stable at about 328 MB. Long-term running test further shows that the algorithm has strong stability in complex environment, and the fluctuation of false alarm rate is only 0.3 %. This algorithm improves the operation and maintenance efficiency of giant hydropower units to a certain extent, and provides technical support for intelligent management of smart power plants.