Static Security Analysis of Power Systems Based on the PCA-DBLS Model

With the in-depth promotion of the digital transformation of the smart grid, the power grid state prediction method based on historical operation data has become a key technology for enhancing the real-time analysis capability of the power system. Aiming at the online calculation bottleneck problem existing in the traditional N-1 static security analysis, this paper proposes an improved Broad Learning System (BLS) that integrates Principal Component Analysis (PCA) and a residual correction mechanism. Firstly, a feature space is constructed through the dimensionality reduction processing of PCA, effectively extracting the essential features of the power grid operation data. Secondly, a dynamic residual correction module is introduced to construct a dual-channel error compensation mechanism, significantly improving the prediction accuracy of the model. Finally, a fast power flow calculation framework based on the improved BLS is established. Compared with the currently common deep neural network prediction models, the method proposed by us greatly improves the training speed and has a high accuracy.