Optimizing Fault Diagnosis in Smart Grids Using Convolutional Neural Networks for Enhanced Speed and Stability

With the increasingly severe global energy crisis and the rapid development of computer and information technology, smart grids have emerged. However, with the transformation to smart grids, the number and types of various terminals, sensors, and new loads in the power system have increased significantly. The massive amount of information and mixed redundant and noisy information accessed by the grid poses a huge challenge for fault handling. Therefore, this article focuses on how to improve fault handling speed in the context of smart grids and how to efficiently and quickly select key fault features to enhance fault handling speed and ensure a stable and reliable power supply system. The study revolves around the optimization method for rapid fault handling based on machine learning. An intelligent grid fault diagnosis model based on convolutional neural networks was built using TensorFlow. The powerful feature extraction and learning capabilities of Convolutional Neural Networks (CNN) were used for fault data training and diagnosis. Experimental analysis was conducted on different aspects to establish fault diagnosis model parameters. The experimental results show that this fault diagnosis model exhibits good performance in terms of training time, accuracy, and stability.