Research on fault prediction and diagnosis system of high-voltage switchgear based on supervised learning algorithm

In the operation of power system, the reliability of high voltage switchgear is very important to maintain the stable operation of the entire power industry. In this paper, a fault prediction and diagnosis system of high voltage switchgear is studied based on supervised learning algorithm. In view of the limitations of traditional fault prediction methods, such as dependence on professional knowledge and low accuracy, this study introduced BP neural network algorithm as the main prediction tool. By collecting a large number of mechanical vibration characteristic signals of high voltage switchgear, the training data set of BP neural network is constructed. The BP neural network model is used to monitor and predict the operating state of the switchgear to realize the timely identification and early warning of potential faults. The system monitors the mechanical characteristics data of different parts of the switch cabinet and key electrical equipment in real time, and analyzes and predicts these data through BP neural network, so as to judge whether there is fault risk. Through the verification and testing in the actual power system, our research results show that the fault prediction and diagnosis system of high voltage switchgear based on BP neural network algorithm has high accuracy and reliability, and can effectively detect potential faults in advance, and take corresponding maintenance measures to ensure the safe and stable operation of the power system. It has important engineering application value and practical significance for the operation of power system.