Perception Algorithm of Converter Station Based on Deep Stability Learning

With the increasing scale and complexity of the power grid, how to accurately perceive the status of the converter station and detect its abnormality in time has become an urgent problem to be solved. However, the traditional converter station perception algorithm has some problems, such as low accuracy, high false positive rate, and limited ability to deal with complex abnormal situations. To address these issues, this paper introduces a perception algorithm based on deep stable learning. Through collected data and data analysis, we evaluate the performance difference of these two algorithms. The results show that the algorithm based on deep stable learning shows a better detection effect in abnormal data perception, with higher precision and recall. The research in this paper shows that the converter station perception algorithm based on deep stability learning has obvious advantages in abnormal data perception.