Research on fault prediction and diagnosis of smart grid based on deep learning

In order to solve the problem of accuracy and real-time fault prediction and diagnosis of smart grid, this paper proposes a fault prediction and diagnosis method based on deep learning. By constructing a deep learning model to extract and analyze the characteristics of power grid operation data, the accurate identification and location of fault types are realized. A fault prediction model based on multi-layer neural network was designed, and a complete fault diagnosis system was established by integrating time series data analysis methods. Experimental results show that the proposed method improves the accuracy of fault prediction by ${1 5 \%}$ and shortens the fault location time by ${3 0 \%}$ compared with the traditional method, which has strong practical value. The research results provide new technical support for the safe and stable operation of smart grid.