Fault prediction technology of smart grid based on big data

The rapid development of computer technology has provided a more scientific solution for fault prediction in smart grids. To conduct more accurate fault prediction for smart grids, the research involves data analysis through empirical wavelet transform algorithms and Teager energy operators, fault prediction through long short-term memory neural networks, and fault diagnosis through convolutional neural networks, thereby constructing a fault prediction model. In the performance verification experiment, the accuracy curve of the model proposed by the research reached an accuracy rate of 98.72% after 50 iterations and tended to stabilize, demonstrating high precision and fast computing speed. The experimental results show that the model proposed in the research demonstrates excellent performance in terms of diagnostic accuracy and computing speed, and can predict faults more accurately to ensure the normal operation of the smart grid.