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A WLAV-based Robust Hybrid State Estimation using Circuit-theoretic Approach

  • Carnegie Mellon University

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

10 Scopus citations

Abstract

For reliable and secure power grid operation, AC state-estimation (ACSE) must provide certain guarantees of convergence while being resilient against bad-data. This paper develops a circuit-theoretic weighted least absolute value (WLAV) based hybrid ACSE that satisfies these needs to overcome some of the limitations of existing ACSE methods. Hybrid refers to the inclusion of RTU and PMU measurement data, and the use of the LAV objective function enables automatic rejection of bad data while providing clear identification of suspicious measurements from the sparse residual vector. Taking advantage of linear construction of the measurement models in circuit-theoretic approach, the proposed hybrid SE is formulated as a LP problem with guaranteed convergence. To address efficiency, we further develop problem-specific heuristics for fast convergence. To validate the efficacy of the proposed approach, we run ACSE on large cases and compare the results against WLS-based algorithms. We further demonstrate the advantages of our solution methodology over standard commercial LP solvers through comparison of runtime and convergence performance.

Original languageEnglish
Title of host publication2021 IEEE Power and Energy Society General Meeting, PESGM 2021
PublisherIEEE Computer Society
ISBN (Electronic)9781665405072
DOIs
StatePublished - 2021
Event2021 IEEE Power and Energy Society General Meeting, PESGM 2021 - Washington, United States
Duration: Jul 26 2021Jul 29 2021

Publication series

NameIEEE Power and Energy Society General Meeting
Volume2021-July
ISSN (Print)1944-9925
ISSN (Electronic)1944-9933

Conference

Conference2021 IEEE Power and Energy Society General Meeting, PESGM 2021
Country/TerritoryUnited States
CityWashington
Period07/26/2107/29/21

Keywords

  • AC state estimation
  • bad data
  • L1-norm
  • least absolute value

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