Intelligent Fault Identification System Integrating Artificial Intelligence and Power Big Data

This study constructs an intelligent fault identification system based on the fusion of artificial intelligence and power big data to improve the efficiency and accuracy of fault detection. First, various data from the power system are collected and preprocessed, including historical fault records, real-time monitoring data, and environmental factors. The data are normalized using the Z-score standardization method to ensure data consistency. Secondly, the PCA feature selection technique is used to extract key information. In the model training stage, CNN and LSTM are combined, the network weights are optimized through the back propagation algorithm, and the cross entropy loss function is used for evaluation. The system can accurately identify the fault type within 3 seconds after the fault occurs, significantly improving the fault response speed. The intelligent fault identification system that integrates artificial intelligence and big data provides effective technical support for the safe operation of the power industry and has broad application prospects.