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Phasor Estimation for Grid Power Monitoring: Least Square vs. Linear Kalman Filter

Author

Listed:
  • Yassine Amirat

    (Institut de Recherche Dupuy de Lôme (UMR CNRS 6027 IRDL), ISEN Yncréa Ouest, 29200 Brest, France)

  • Zakarya Oubrahim

    (AKKA Technologies Group, 75008 Paris, France)

  • Hafiz Ahmed

    (School of Mechanical, Aerospace and Automotive Engineering, Coventry University, Coventry CV1 5FB, UK)

  • Mohamed Benbouzid

    (Institut de Recherche Dupuy de Lôme (UMR CNRS 6027 IRDL), University of Brest, 29238 Brest, France
    Logistics Engineering College, Shanghai Maritime University, Shanghai 201306, China)

  • Tianzhen Wang

    (Logistics Engineering College, Shanghai Maritime University, Shanghai 201306, China)

Abstract

This paper deals with a comparative study of two phasor estimators based on the least square (LS) and the linear Kalman filter (KF) methods, while assuming that the fundamental frequency is unknown. To solve this issue, the maximum likelihood technique is used with an iterative Newton–Raphson-based algorithm that allows minimizing the likelihood function. Both least square (LSE) and Kalman filter estimators (KFE) are evaluated using simulated and real power system events data. The obtained results clearly show that the LS-based technique yields the highest statistical performance and has a lower computation complexity.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:10:p:2456-:d:357642
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    References listed on IDEAS

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    1. 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.
    2. Marilyn A. Brown & Shan Zhou, 2013. "Smart-grid policies: an international review," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 2(2), pages 121-139, March.
    3. Paolo Castello & Carlo Muscas & Paolo Attilio Pegoraro & Sara Sulis, 2019. "PMU’s Behavior with Flicker-Generating Voltage Fluctuations: An Experimental Analysis," Energies, MDPI, vol. 12(17), pages 1-14, August.
    4. Khokhar, Suhail & Mohd Zin, Abdullah Asuhaimi B. & Mokhtar, Ahmad Safawi B. & Pesaran, Mahmoud, 2015. "A comprehensive overview on signal processing and artificial intelligence techniques applications in classification of power quality disturbances," Renewable and Sustainable Energy Reviews, Elsevier, vol. 51(C), pages 1650-1663.
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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. Hafiz Ahmed & Samet Biricik & Elhoussin Elbouchikhi & Mohamed Benbouzid, 2020. "Adaptive Filtering-Based Pseudo Open-Loop Three-Phase Grid-Synchronization Technique," Energies, MDPI, vol. 13(11), pages 1-14, June.

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