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Fast nonconvex SDP solver for large-scale power system state estimation

  • Xi'an Jiaotong University
  • University of Texas at Austin

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

1 Scopus citations

Abstract

Fast power system state estimation (SE) solution is indispensable to achieve real-time decision making in power grid management. Semidefinite programming (SDP) reformulation has shown powerful to approach the global optimum of the nonlinear SE problem, while suffering from high computational complexity. Thus, we leverage the recent advances in nonconvex SDP reformulation that can allow first-order updates to potentially solve the original SDP problem. We further adopt the accelerated gradient descent (AGD) method for the resultant unconstrained problem for improved convergence speed. Numerical tests have demonstrated that AGD can achieve comparable SE performance as the globally optimal SDP solution at improved computational efficiency.

Original languageEnglish
Title of host publication2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages870-874
Number of pages5
ISBN (Electronic)9781728112954
DOIs
StatePublished - 2 Jul 2018
Externally publishedYes
Event2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018 - Anaheim, United States
Duration: 26 Nov 201829 Nov 2018

Publication series

Name2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018 - Proceedings

Conference

Conference2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018
Country/TerritoryUnited States
CityAnaheim
Period26/11/1829/11/18

Keywords

  • Accelerated gradient descent.
  • Nonconvex reformulation
  • Power system state estimation
  • Semidefinite programming

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