Power System Operation Stability Assessment Method Based on Deep Convolutional Neural Network

INTRODUCTION: For the assessment of power system stability, a power system assessment method based on a deep convolutional neural network is studied. OBJECTIVES: Through the improvement of the integrated convolutional neural network (CNN) network model, the impact of insufficient transient stability assessment caused by sample misjudgment and sample omission is effectively reduced. METHODS: We adopt the hierarchical real-time prediction model to evaluate the stability and instability of the determined stable samples and unstable samples, thereby improving the timeliness and accuracy of transient evaluation. RESULTS: Through experimental comparison, the integrated CNN network model in this study has obvious advantages in accuracy compared with the single CNN network. Compared with other algorithm reference models, this model has a higher evaluation accuracy of 98.39%, far exceeding other comparison models. CONCLUSION: By further evaluating the model’s accuracy, it is proved that the model can provide an effective reference for the follow-up power system stability prevention and has important application value.