Power Grid Stability Analysis Based on Artificial Intelligence Convolutional Neural Networks

Power grid stability is key to reliable operation. The rise of renewables and growing loads demand better methods to ensure power supply quality. To address these issues, this paper proposes a method for power grid stability analysis based on Convolutional Neural Networks (CNN). First, the original grid data is preprocessed, and classification features are one-hot encoded. Then, a deep learning model based on CNN is designed and trained. Finally, the effectiveness of the proposed method is verified through case analysis. The case study compares fully connected neural networks with CNN, and the results show that CNN outperforms fully connected networks in accuracy, precision, recall, and F1 score.