Linearizing Power Flow Model: A Hybrid Physical Model-Driven and Data-Driven Approach
Yi Tan, Yuanyang Chen, Yong Li, Yijia Cao
Abstract
Linear power flow model is advantageous for the fast operational analysis and the efficient optimization of the power systems. In this letter, we propose a hybrid physical model-driven and data-driven approach for linearizing power flow model. In this proposed approach, the linear power flow model contains two parts, i.e., the existing physical-equation-based linear power flow model and the linearized error model. The linearized errors are obtained by the partial least squares regression based data-driven approach. The proposed linear power flow model can retain the useful inherent information from the physical model and utilize the ability of data analysis to extract the inexplicit linear relationship. Simulations on the four test systems have validated that the proposed hybrid linear model exhibits a much better performance on the branch power flow calculation than other linear power flow models.