OPTIMIZING POWER FLOW DATA: IMPLEMENTING A WASSERSTEINGRADIENT FLOW SAMPLING AND REPLENISHMENT STRATEGY

Abstract

The application of artificial intelligence (AI) methods to grid analysis has been extensively studied. The distribution characteristics of the power flow dataset required for the training of AI methods will affect the performance of AI models. The power flow data accumulated for offline analysis are manually adjusted limit operation mode and distributed at the grid operation boundary, so the power flow dataset for offline analysis has good distribution characteristics. However, its small number and low manual generation efficiency make it difficult to exploit the advantages of this distributed characteristic dataset. In this paper, a power flow dataset sample supplementation method based on Wasserstein-gradient flow is proposed to realize the adjustment of the power flow dataset considering the distribution characteristics by solving the dynamic process of the dataset for Wassersteingradient flow. It is also tested on the CEPRI-36 node grid power flow dataset, and the generated supplemental data all have similar distribution characteristics with the target dataset, which verifies the effectiveness of the method