Data-Driven Power Flow Linearization: A Regression Approach
For power system operators and planners, this work offers a more accurate linearization method that leverages available measurement data, though it is an incremental improvement over existing model-based approaches.
This paper proposes a data-driven approach for power flow linearization using regression models, achieving higher accuracy than traditional model-based methods on IEEE standard cases.
The linearization of a power flow (PF) model is an important approach for simplifying and accelerating the calculation of a power system's control, operation, and optimization. Traditional model-based methods derive linearized PF models by making approximations in the analytical PF model according to the physical characteristics of the power system. Today, more measurements of the power system are available and thus facilitate data-driven approaches beyond model-driven approaches. This work studies a linearized PF model through a data-driven approach. Both a forward regression model ((P, Q) as a function of (theta, V)) and an inverse regression model ((theta, V) as a function of (P, Q)) are proposed. Partial least square (PLS)- and Bayesian linear regression (BLR)-based algorithms are designed to address data collinearity and avoid overfitting. The proposed approach 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 approach can realize a higher calculation accuracy than model-based approaches can. The results also demonstrate that the obtained regression parameter matrices of data-driven models reflect power system physics by demonstrating similar patterns with some power system matrices (e.g., the admittance matrix).