Distributed state estimation for AC power systems using Gauss-Newton ALADIN

Authors

Xu Du, Alexander Engelmann, Yuning Jiang, Timm Faulwasser, Boris Houska.

Reference

In Proceedings of the
58th IEEE Conference on Decision and Control,
pages 1919 - 1924, Nice, France, December, 2019.

Abstract

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 an efficient (computationally and communicationally) variant of ALADIN for network maximum likelihood estimation problems. Analyzing an IEEE 30-Bus system we illustrate how the proposed algorithm can be used to solve highly nontrivial network state estimation problems. We also compare the method with existing distributed parameter estimation codes in order to illustrate its performance.

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Bibtex

@INPROCEEDINGS{Du2019,
author = {Du, X. and Engelmann, A. and Jiang, Y. and Faulwasser, T. and Houska, B.},
title = {Distributed state estimation for {AC} power systems using {G}auss-{N}ewton {ALADIN}},
booktitle = {In Proceedings of the 58th IEEE Conference on Decision and Control},
year = {2019},
pages = {1919–1924},
}