Simulation of Power System Fault Detection and Prediction Model Based on Deep Learning

The reliability and stability of power system are very important to modern society. However, faults in the power system may lead to power supply interruption, loss and potential security risks. Therefore, it is urgent to develop an efficient fault detection and prediction model for power system. The purpose of this study is to explore the method based on deep learning to improve the security and reliability of power system. In this study, the recurrent neural network (RNN) is used to train and test the simulation data of power system. In addition, the time series characteristics of different fault lines or different fault States are learned through attention mechanism, thus accelerating the efficiency of network learning. The research results show that deep learning has great potential in power system fault detection, which can effectively capture the complex characteristics of power system and improve the accuracy of fault detection. The model based on deep learning can help power companies identify potential faults in advance, thus reducing power outage time and losses. Power system fault detection and prediction models based on deep learning provide powerful tools for improving the reliability and security of power systems. However, the successful application of these models requires comprehensive data support and continuous performance monitoring and improvement.