Maximum Likelihood Estimation for Line Parameters in Distribution Grids Based on Expectation Maximization Algorithm

This paper proposes a method for obtaining nonlinear models of distribution grid based on available measurements from the power grid. We formulate a maximum likelihood estimation (MLE) problem that estimates unknown line parameters—specifically, the impedance between nodes—using measured voltage magnitudes and net active and reactive power injections at each node. The nonlinear model for the distribution grid uses a nonlinear approximation of the DistFlow model, which includes line losses and is parameterized by the unknown line impedances. We solve the resulting MLE problem using an expectation maximization (EM) algorithm, tailored for the nonlinear model, and provide a numerically robust implementation. The proposed method is demonstrated on the IEEE 37-node test network, and we compare it with the state-of-the art methods. The proposed method achieves a 70% reduction in voltage error and an error for state variables that is more than 10000 times lower. A final comparison uses data from a real network, and the proposed method achieves parameter estimates with errors 100 times lower than competing methods.