1 research outputs found
Distributed State Estimation for AC Power Systems using Gauss-Newton ALADIN
This paper proposes a structure exploiting algorithm for solving non-convex
power system state estimation problems in distributed fashion. Because the
power flow equations in large electrical grid networks are non-convex equality
constraints, we develop a tailored state estimator based on Augmented
Lagrangian Alternating Direction Inexact Newton (ALADIN) method, which can
handle the nonlinearities efficiently. Here, our focus is on using Gauss-Newton
Hessian approximations within ALADIN in order to arrive at at an efficient
(computationally and communicationally) variant of ALADIN for network maximum
likelihood estimation problems. Analyzing the IEEE 30-Bus system we illustrate
how the proposed algorithm can be used to solve highly non-trivial network
state estimation problems. We also compare the method with existing distributed
parameter estimation codes in order to illustrate its performance