A Performance Comparison Between Extended Kalman Filter and Unscented Kalman Filter in Power System Dynamic State Estimation

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Abstract

Dynamic State Estimation (DSE) is a critical tool for analysis, monitoring and planning of a power system. The concept of DSE involves designing state estimation with Extended Kalman Filter (EKF) or Unscented Kalman Filter (UKF) methods, which can be used by wide area monitoring to improve the stability of power system. State estimation with EKF and UKF methods can be used for monitoring and estimating the dynamic state variables of multi-machine power systems, which are generator rotor speed and rotor angle. This paper uses Powerfactory to solve power flow analysis of simulations, then a non-linear state estimator is developed in MatLab to solve states by applying the unscented Kalman filter (UKF) and Extended Kalman Filter (EKF) algorithm. Finally, a DSE model is built for a 14 bus power system network to evaluate the proposed algorithm for the networks.This article will focus on comparing and studying the advantages and disadvantages of both methods under transient conditions. It is demonstrated that UKF is easier to implement and accurate in estimation.
Original languageEnglish
Title of host publicationProceedings of 2016 51st International Universities' Power Engineering Conference (UPEC)
Number of pages6
Place of PublicationCoimbra, Portugal
PublisherIEEE Press
Publication dateSep 2016
ISBN (Electronic)978-1-5090-4650-8
DOIs
Publication statusPublished - Sep 2016
Event2016 51st International Universities' Power Engineering Conference (UPEC) - Coimbra, Portugal
Duration: 6 Sep 20169 Sep 2016

Conference

Conference2016 51st International Universities' Power Engineering Conference (UPEC)
CountryPortugal
CityCoimbra
Period06/09/201609/09/2016

Keywords

  • Dynamic State Estimation
  • Extended Kalman Filter
  • Power System Transients
  • State Estimation
  • Unscented Kalman Filter

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