Du, XuXuDuEngelmann, AlexanderAlexanderEngelmannJiang, YuningYuningJiangFaulwasser, TimmTimmFaulwasserHouska, BorisBorisHouska2024-03-052024-03-052019-12-01Proceedings of the 58th IEEE Conference on Decision and Control 2019: 1919-1924 (2019)9781728113982https://hdl.handle.net/11420/46215This 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 these nonlinearities efficiently. Here, our focus is on using Gauss-Newton Hessian approximations within ALADIN to arrive 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 non-trivial network state estimation problems. We also compare the method with existing distributed parameter estimation codes in order to illustrate its performance.enConstrained optimizationDistributed parameter control systemsElectric load flowMatrix algebraState estimationComputer SciencesMathematicsConference Paper10.1109/CDC40024.2019.9028966Other