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An Extended Kalman Filter Approach for Accurate Instantaneous Dynamic Phasor Estimation

Author

Listed:
  • Matilde De Apráiz

    (Department Computer Science and Electronics, University of Cantabria, 39005 Santander, Spain)

  • Ramón I. Diego

    (Department Computer Science and Electronics, University of Cantabria, 39005 Santander, Spain)

  • Julio Barros

    (Department Computer Science and Electronics, University of Cantabria, 39005 Santander, Spain)

Abstract

This paper proposes the application of a non-linear Extended Kalman Filter (EKF) for accurate instantaneous dynamic phasor estimation. An EKF-based algorithm is proposed to better adapt to the dynamic measurement requirements and to provide real-time tracking of the fundamental harmonic components and power system frequencies. This method is evaluated using dynamic compliance tests defined in the IEEE C37.118.1-2011 synchrophasor measurement standard, providing promising results in phasor and frequency estimation, compliant with the accuracy required in the case of off-nominal frequency, amplitude and phase angle modulations, frequency ramps, and step changes in magnitude and phase angle. An important additional feature of the method is its capability for real-time detection of transient disturbances in voltage or current waveforms using the residual of the filter, which enables flagging of the estimation for suitable processing.

Suggested Citation

  • Matilde De Apráiz & Ramón I. Diego & Julio Barros, 2018. "An Extended Kalman Filter Approach for Accurate Instantaneous Dynamic Phasor Estimation," Energies, MDPI, vol. 11(11), pages 1-11, October.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:11:p:2918-:d:178420
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    Citations

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    Cited by:

    1. Babak Jafarpisheh & Anamitra Pal, 2021. "A Robust Algorithm for Real-Time Phasor and Frequency Estimation under Diverse System Conditions," Energies, MDPI, vol. 14(21), pages 1-20, November.
    2. Yassine Amirat & Zakarya Oubrahim & Hafiz Ahmed & Mohamed Benbouzid & Tianzhen Wang, 2020. "Phasor Estimation for Grid Power Monitoring: Least Square vs. Linear Kalman Filter," Energies, MDPI, vol. 13(10), pages 1-15, May.
    3. Juan-José González de-la-Rosa & Manuel Pérez-Donsión, 2020. "Special Issue “Analysis for Power Quality Monitoring”," Energies, MDPI, vol. 13(3), pages 1-6, January.
    4. Malgorzata Binek & Andrzej Kanicki & Pawel Rozga, 2021. "Application of an Artificial Neural Network for Measurements of Synchrophasor Indicators in the Power System," Energies, MDPI, vol. 14(9), pages 1-14, April.
    5. Zakarya Oubrahim & Yassine Amirat & Mohamed Benbouzid & Mohammed Ouassaid, 2023. "Power Quality Disturbances Characterization Using Signal Processing and Pattern Recognition Techniques: A Comprehensive Review," Energies, MDPI, vol. 16(6), pages 1-41, March.

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