Research on Intelligent Fault Prediction and Protection Control Mechanism for Electrical Equipment in New Power Systems

This paper proposes an intelligent fault prediction and protection control mechanism for electrical equipment to address the problems of traditional protection response lag and poor adaptability caused by the high proportion of new energy integration and complex operation in the new power system. The method integrates multi-source data such as electrical, environmental, and image data to construct a deep learning model of 1D-CNN BiLSTM+lightweight CNN for accurate fault recognition; And design a "central perception edge decision-making hierarchical control" architecture, combining adaptive strategy logic and edge deployment to achieve local rapid response. The simulation results show that the fault recognition time of this mechanism is shortened to 26 ms, the total clearing delay is reduced by 70%, and the average recognition accuracy is 96.5%, which is significantly better than traditional methods and has good engineering application prospects.