Application of Artificial Intelligence Technology in Power System Stability Assessment

Abstract

In order to realize power system stability assessment, the author studies a deep Convolutional neural network algorithm for power system operation stability assessment. Combining short-term simulation with neural network prediction to reduce the time required for transient stability analysis can be used in various simulation analysis scenarios. A deep Convolutional neural network algorithm is used to improve the performance of the security system. A deep Convolutional Neural Network (CNN) is used to construct a multi-layer and multi-column neural network to evaluate the stability of electrical networks. The system significantly improves the efficiency of evaluating specific faults, particularly enhancing the specificity of evaluation judgments, and reducing the additional maintenance workload caused by providing stability warnings for the operation status of faultless power grids. The experimental results indicate that, the system has shown certain advantages in terms of sensitivity to the judgment of various types of faults mentioned above, especially improving the sensitivity to other faults with unknown causes, which is 10.8 percentage points higher than the previous system. The smallest difference in the improvement of sensitivity indicators is the sensitivity to ground faults. The previous system reached 95.4%, while this system increased by 3.9 percentage points to 99.3%. It is proved that the deep CNN is applicable to the task of power system operation stability assessment.