Research and Application of Intelligent Inspection Technology for Hydropower Stations Based on Image Recognition Technology

As an important energy supply unit, the safety and reliability of the equipment of hydropower station is crucial to the stable operation of the power system. The traditional inspection method of hydroelectric power stations has the problems of low efficiency and high labor cost. This paper uses an intelligent inspection technology based on convolutional neural network (CNN) for hydroelectric power station inspection. This study presents how to collect the image data of various devices in the hydropower station, and how to preprocess the data. Then, the study uses the CNN model for training, and learning. The CNN will automatically extract features of the equipment images. Lastly, we propose the abnormality identification and early warning of equipment using the algorithms of fault diagnosis and condition monitoring. Research findings indicate that the CNN-based intelligent monitoring technology developed for hydroelectric power plants is able to perform well in the equipment's abnormality identification and early warning. It can accurately detect the abnormal state and the potential defects in the equipment, and provide timely warning or feedback.