Physics

Nonlinear system

Overfitting

Mathematics

Algorithm

Statistics

Mathematical optimization

Linearization

Artificial intelligence

Power (physics)

Control (management)

Monte Carlo method

Computer science

Quantum mechanics

Electric power system

Control theory (sociology)

Artificial neural network

IEEE Transactions on Smart Grid

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 paper studies a linearized PF model through a datadriven approach. Both a forward regression model [(F, Q) as a function of (8, V)] and an inverse regression model [(8, V) as a function of (F, Q)] are proposed. Partial least squaresand Bayesian linear regression-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).

Data-Driven Power Flow Linearization: A Regression Approach