摘要：With the rapid development of urban power grids, distributed renewable energy sources and adjustable loads have increased significantly, resulting in more complex operation conditions, increasing the difficulty of power flow calculations. The usage of artificial intelligence technology to assist in calculating power flows for large-scale urban grids has a wide range of application prospects. It is currently difficult to generate enough grid operation database with controlled distribution for artificial intelligence (AI) method research. Data is one of the important factors affecting the performance of deep learning algorithms, and the lack of research on data distribution characteristics also hinders the performance of deep learning algorithms. The distributional characteristics of data sets in high-dimensional feature spaces are difficult to represent and measure, and the algorithm design process is prone to encounter curse of dimensionality. This paper proposed a novel method for generating databases to improve the solving efficiency of data-driven power flow calculation problems. The proposed method removes samples based on the characteristics of data distribution. It constructs two databases, namely the blue noise distribution database and the variable density boundary enhanced distribution database. Compared with the classical stochastic sampling database, the proposed boundary-enhanced variable density (BEVD) database has significantly improved the judgment accuracy of power flow convergence. Finally, the China Electric Power Research Institute-36 (CEPRI-36) bus system is used to verify the effectiveness of the proposed method. The judgment accuracy was improved by 2.91%–9.5%.
