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

Yassine Amirat, Zakarya Oubrahim, Hafiz Ahmed, Mohamed Benbouzid, Tianzhen Wang

    Research output: Contribution to journalArticlepeer-review

    19 Citations (Scopus)
    37 Downloads (Pure)


    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
    Original languageEnglish
    Article number2456
    Number of pages15
    Issue number10
    Publication statusPublished - 13 May 2020

    Bibliographical note

    c 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (


    • IEEE standard C37.118
    • Kalman filter estimation (KFE)
    • Least square estimation (LSE)
    • Phasor and frequency estimation
    • Phasor measurement units
    • Power quality monitoring

    ASJC Scopus subject areas

    • Renewable Energy, Sustainability and the Environment
    • Energy Engineering and Power Technology
    • Energy (miscellaneous)
    • Control and Optimization
    • Electrical and Electronic Engineering


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