Fast LAV Estimation via Composite Optimization

Gang Wang, Gerogios B. Giannakis, Jie Chen

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

1 Scopus citations


Accurate and robust power system state estimation (PSSE) is an essential prerequisite for reliable operation of smart power grids. In contrast to the commonly employed weighted least squares (WLS) one, the least-absolute-value (LAV) estimator is well documented for its robustness. Due to the non-convexity and non-smoothness however, existing LAV implementations are typically slow, thus inadequate for real-time system monitoring. In this context, this paper puts forward a novel LAV estimator leveraging recent algorithmic advances in composite optimization. Concretely, the estimator is based on a proximal linear procedure that deals with a sequence of convex quadratic problems, each efficiently solvable by means of either standard convex optimization methods, or the alternating direction method of multipliers. Simulated tests using two IEEE benchmark networks showcase its improved robustness and computational efficiency relative to several competing alternatives.

Original languageEnglish (US)
Title of host publication2019 IEEE Power and Energy Society General Meeting, PESGM 2019
PublisherIEEE Computer Society
ISBN (Electronic)9781728119816
StatePublished - Aug 2019
Externally publishedYes
Event2019 IEEE Power and Energy Society General Meeting, PESGM 2019 - Atlanta, United States
Duration: Aug 4 2019Aug 8 2019

Publication series

NameIEEE Power and Energy Society General Meeting
ISSN (Print)1944-9925
ISSN (Electronic)1944-9933


Conference2019 IEEE Power and Energy Society General Meeting, PESGM 2019
Country/TerritoryUnited States

Bibliographical note

Publisher Copyright:
© 2019 IEEE.


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