An Improved Dynamic Phasor Tracking Algorithm Using Iterative Unscented Kalman

  • Xiong-bo Xiao
  • Li Xia
  • Li-ming Wang
  • Yan-dong Wang
Conference paper


This paper presents an improved iterative unscented Kalman tracking algorithm to estimate dynamic phasor, establishes a model considering the change rate of power frequency and power components, dynamic phasor and other electrical parameters are estimated by adaptive IUKF algorithm, the estimate accuracy is improved. Numerical simulation shows that the effectiveness of the proposed frequency tracking algorithm as well as the adaptability of the harmonic and noise.


Dynamic phasor tracking Frequency estimation IUKF 


  1. 1.
    Luo C, Zhang M (2008) Frequency tracking of distorted power signal using complex sigma point Kalman filter. Autom Electr Power Syst 32(13):35–38MathSciNetGoogle Scholar
  2. 2.
    Zhao R, Ma S, Li H (2013) Strong tracking filter based frequency-measuring algorithm for power system. Power Syst Prot Control 41(7):85–90Google Scholar
  3. 3.
    Shi Y, Han CZ (2011) Adaptive UKF method with applications to target tracking. Acta Automatica Sinca 37(6):755–759MathSciNetGoogle Scholar
  4. 4.
    Qu Z, Yao Y, Han J (2009) State estimation of permanent magnet synchronous motor using modified square-root UKF algorithm. Electric Mach Control 13(3):452–457Google Scholar
  5. 5.
    Regulski P, Terzija V (2012) Estimation of frequency and fundamental power components using an unscented Kalman filter. IEEE Trans Instr 61(4):952–962CrossRefGoogle Scholar
  6. 6.
    Bolognani S, Obde O, Zigliotto M (1999) Sensorless full-digital PMSM drive with EKF estimation of speed and rotor position. IEEE Trans Industr Electron 46(1):184–191CrossRefGoogle Scholar
  7. 7.
    Li Y, Li Z (2012) Adaptive noise unscented particle filter under unknown circumstances. Journal Jilin Univ (Eng Technol Ed) 10(3):20–27Google Scholar
  8. 8.
    Mai R, He Z, Bo Z (2009) Research on synchronized phasor measurement algorithm under dynamic conditions. Proc CSEE 29(10):52–58Google Scholar

Copyright information

© Atlantis Press and the author(s) 2016

Authors and Affiliations

  • Xiong-bo Xiao
    • 1
  • Li Xia
    • 1
  • Li-ming Wang
    • 1
  • Yan-dong Wang
    • 1
  1. 1.Department of Intelligent EngineeringNaval University of EngineeringWuhanChina

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