Search direction optimization of power flow analysis based on physics-informed deep learning

Abstract

Power flow analysis is crucial for obtaining power system operation states and optimizing control measures. The increasing integration of renewable energy sources has resulted in a more complex power system, posing challenges to the computational efficiency and convergence of conventional power analysis methods. Based on the physics-informed deep learning, this paper proposes an optimization scheme for the search direction to improve the performance of power flow analysis. The higher-order information originating from the Taylor series expansion of the power flow equation is utilized to optimize the search direction. The deep belief network is used to establish a nonlinear mapping between the power flow equations and the optimized search direction. Additionally, the physical information of the power system is encoded into the deep learning model to meet the real physical constraints. Case study results show that the proposed scheme contributes to improve the computational efficiency and convergence in power analysis, and is feasible for the scenarios of ill-conditioned power flow.