Data-driven boundary regression for equivalent identification of external parameters in steady-state models of power systems

Abstract

External Network Modeling (ENM) is an important method to simplify and accelerate power system control, protection, and optimization. Traditional model-based methods obtain accurate external parameters by analyzing the physical properties of the whole system. In the case of incomplete physical information, the use of data-driven models has become an effective means to analyze the operating state of power systems. In this paper, a data-driven approach is used to propose an external equivalent parameter identification framework for steady-state models of power systems to accurately estimate the operating states of power systems of interest. Linear correction coefficients are used to deal with the challenges brought by the nonconvexities of the power flow equation to the regression. Based on the linearly mapped power flow equation, we derive the Multivariate Linear Regression (MLR) model of the external power system. The Box-Cox transformation (BCT) and ridge regression algorithm (RRA) algorithms are designed to address data normality, collinearity, and overfitting. The proposed framework is tested on a series of IEEE standard cases, which include both meshed transmission grids and radial distribution grids, with both Monte Carlo simulated data and public testing data. The results show that the proposed method can obtain a higher calculation accuracy than model-based methods can. The results also demonstrate that the proposed data-driven equivalent framework can obtain accurate regression parameter matrices under the condition of non-normal data, and accurately reflect the physical parameters of the external power system.