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 journalArticle

1 Citation (Scopus)
3 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 (


  • phasor and frequency estimation;
  • kalman filter estimation (KFE)
  • least square estimation (LSE)
  • phasor measurement units
  • IEEE standard C37.118
  • power quality monitoring

Fingerprint Dive into the research topics of 'Phasor Estimation for Grid Power Monitoring: Least Square vs. Linear Kalman Filter'. Together they form a unique fingerprint.

  • Cite this