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Distributed state estimation for AC power systems using Gauss-Newton ALADIN
Publikationstyp
Conference Paper
Date Issued
2019-12-01
Sprache
English
Author(s)
Volume
2019-December
Start Page
1919
End Page
1924
Article Number
9028966
Citation
Proceedings of the 58th IEEE Conference on Decision and Control 2019: 1919-1924 (2019)
Contribution to Conference
Publisher DOI
Scopus ID
Publisher
IEEE
ISSN
07431546
ISBN
9781728113982
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 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.
Subjects
Constrained optimization
Distributed parameter control systems
Electric load flow
Matrix algebra
State estimation
DDC Class
004: Computer Sciences
510: Mathematics