AI Prediction of Power Grid Faults Based on Deep Learning and Improvement of Emergency Response Efficiency in Automated Repair

This study aims to enhance the stability and reliability of power grids by proposing a deep learning-based emergency response method for power grid fault prediction and automated repair. We have constructed a fault prediction model capable of capturing the spatio-temporal characteristics of power grid data and the complex relationships between equipment. This model is designed to handle the heterogeneity of various data sources within the power grid, such as dissolved gas data and oil temperature data, and it independently analyzes these data to enhance prediction accuracy. Furthermore, based on Fourier transform technology, this study optimizes automated repair emergency response through frequency domain analysis. In simulations of a three-machine nine-node system, the proposed method has demonstrated improvements in fault detection speed, model interpretability, prediction accuracy, and response time delay. Specifically, the detection accuracy for single-phase-to-ground faults reached 94.7%, two-phase-to-phase faults 91%, two-phase-to-ground faults 95.4%, and three-phase faults 93%. Additionally, when considering the overall performance across these different fault types, the method achieved a comprehensive solution accuracy of 93.8% during the testing phase, which takes into account the detection rates of all mentioned fault types. The research method effectively enhances the accuracy of power grid fault prediction and the response speed of automated repair, providing strong technical support for the intelligent management and maintenance of power grids. The results of this study not only advance the development of power grid fault prediction technology in theory but also hold significant value and broad application prospects in practical applications.