A Power Grid Stability Analysis Method Based on CNN

As power systems' interconnection scale expands, the methods for analyzing power grid stability need further optimization. This paper proposes a power grid stability analysis method based on Convolutional Neural Networks(CNN). We model the power grid stability problem as a binary classification problem. Firstly, we provide a detailed explanation of the power grid characteristic data and conduct data preprocessing. Subsequently, due to the advantages of CNN in processing spatially structured data, we address this problem. Finally, we obtain a new power grid stability analysis model through case-based comparative analysis. The results indicate that the proposed model outperforms traditional analysis methods in terms of classification accuracy, recall rate, and F1-score.