Advances in decentralized state estimation for power systems

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations

Abstract

Distributed learning via network diffusion is a popular trend in signal processing, which addresses the need of obtaining scalable analytics from networked sensor systems. This paper describes relevant advances in distributed power system state estimation (PSSE) via diffusion. Considering a hybrid sensor measurements system, we show that the Gauss-Newton approach, typically favored in PSSE, can be used as a primitive to derive a gossip-based algorithm that outperforms first order diffusion methods proposed in the literature. We also study analytically and numerically the dependency between measurement placement, grid topology and physical parameters, communication network and the performance of the decentralized PSSE.

Original languageEnglish (US)
Title of host publication2013 5th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2013
Pages428-431
Number of pages4
DOIs
StatePublished - Dec 1 2013
Externally publishedYes
Event2013 5th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2013 - Saint Martin, France
Duration: Dec 15 2013Dec 18 2013

Publication series

Name2013 5th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2013

Other

Other2013 5th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2013
CountryFrance
CitySaint Martin
Period12/15/1312/18/13

ASJC Scopus subject areas

  • Computer Science Applications

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