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

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Abstrakt

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.
OriginalsprogEngelsk
TitelProceedings of 2016 51st International Universities' Power Engineering Conference (UPEC)
Antal sider6
UdgivelsesstedCoimbra, Portugal
ForlagIEEE Press
Publikationsdatosep. 2016
ISBN (Elektronisk)978-1-5090-4650-8
DOI
StatusUdgivet - sep. 2016
Begivenhed2016 51st International Universities' Power Engineering Conference (UPEC) - Coimbra, Portugal
Varighed: 6 sep. 20169 sep. 2016

Konference

Konference2016 51st International Universities' Power Engineering Conference (UPEC)
LandPortugal
ByCoimbra
Periode06/09/201609/09/2016

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