IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v13y2020i10p2456-d357642.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/13/10/2456/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/13/10/2456/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Igual, R. & Medrano, C., 2020. "Research challenges in real-time classification of power quality disturbances applicable to microgrids: A systematic review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 132(C).
    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. Md Shafiullah & M. A. Abido & Taher Abdel-Fattah, 2018. "Distribution Grids Fault Location employing ST based Optimized Machine Learning Approach," Energies, MDPI, vol. 11(9), pages 1-23, September.
    5. Boza, Pal & Evgeniou, Theodoros, 2021. "Artificial intelligence to support the integration of variable renewable energy sources to the power system," Applied Energy, Elsevier, vol. 290(C).
    6. Stavros Lazarou & Sofoklis Makridis, 2017. "Hydrogen Storage Technologies for Smart Grid Applications," Challenges, MDPI, vol. 8(1), pages 1-11, June.
    7. Matej Žnidarec & Zvonimir Klaić & Damir Šljivac & Boris Dumnić, 2019. "Harmonic Distortion Prediction Model of a Grid-Tie Photovoltaic Inverter Using an Artificial Neural Network," Energies, MDPI, vol. 12(5), pages 1-19, February.
    8. Aleksander Chudy & Piotr Hołyszko & Paweł Mazurek, 2022. "Fast Charging of an Electric Bus Fleet and Its Impact on the Power Quality Based on On-Site Measurements," Energies, MDPI, vol. 15(15), pages 1-16, July.
    9. Seung-Mo Je & Hanchul Woo & Jaehyeon Choi & Se-Hoon Jung & Jun-Ho Huh, 2022. "A Research Trend on Anonymous Signature and Authentication Methods for Privacy Invasion Preventability on Smart Grid and Power Plant Environments," Energies, MDPI, vol. 15(12), pages 1-20, June.
    10. Majidi Nezhad, Meysam & Neshat, Mehdi & Piras, Giuseppe & Astiaso Garcia, Davide, 2022. "Sites exploring prioritisation of offshore wind energy potential and mapping for wind farms installation: Iranian islands case studies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 168(C).
    11. Francisco G. Montoya & Raul Baños & Alfredo Alcayde & Maria G. Montoya & Francisco Manzano-Agugliaro, 2018. "Power Quality: Scientific Collaboration Networks and Research Trends," Energies, MDPI, vol. 11(8), pages 1-16, August.
    12. Shao, Han & Henriques, Rui & Morais, Hugo & Tedeschi, Elisabetta, 2024. "Power quality monitoring in electric grid integrating offshore wind energy: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 191(C).
    13. Xiaojing Sun & Marilyn A. Brown & Matt Cox & Roderick Jackson, 2016. "Mandating better buildings: a global review of building codes and prospects for improvement in the United States," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 5(2), pages 188-215, March.
    14. Mehigan, L. & Deane, J.P. & Gallachóir, B.P.Ó. & Bertsch, V., 2018. "A review of the role of distributed generation (DG) in future electricity systems," Energy, Elsevier, vol. 163(C), pages 822-836.
    15. 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.
    16. Xin Liu & Bangxin Zhao & Wenqing He, 2020. "Simultaneous Feature Selection and Classification for Data-Adaptive Kernel-Penalized SVM," Mathematics, MDPI, vol. 8(10), pages 1-22, October.
    17. Jennie C. Stephens & Elizabeth J. Wilson & Tarla R. Peterson & James Meadowcroft, 2013. "Getting Smart? Climate Change and the Electric Grid," Challenges, MDPI, vol. 4(2), pages 1-16, September.
    18. Haitao Gao & Peng Xu & Jin Tao & Shihui Huang & Rugang Wang & Quan Zhou, 2020. "Voltage Flicker Detection Based on Probability Resampling," Energies, MDPI, vol. 13(13), pages 1-12, June.
    19. Misael Lopez-Ramirez & Luis Ledesma-Carrillo & Eduardo Cabal-Yepez & Carlos Rodriguez-Donate & Homero Miranda-Vidales & Arturo Garcia-Perez, 2016. "EMD-Based Feature Extraction for Power Quality Disturbance Classification Using Moments," Energies, MDPI, vol. 9(7), pages 1-15, July.
    20. Lund, Peter D. & Lindgren, Juuso & Mikkola, Jani & Salpakari, Jyri, 2015. "Review of energy system flexibility measures to enable high levels of variable renewable electricity," Renewable and Sustainable Energy Reviews, Elsevier, vol. 45(C), pages 785-807.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:13:y:2020:i:10:p:2456-:d:357642. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.