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

Yassine Amirat, Zakarya Oubrahim, Hafiz Ahmed, Mohamed Benbouzid and Tianzhen Wang
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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
Tianzhen Wang: Logistics Engineering College, Shanghai Maritime University, Shanghai 201306, China

Energies, 2020, vol. 13, issue 10, 1-15

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.

Keywords: phasor and frequency estimation; kalman filter estimation (KFE); least square estimation (LSE); phasor measurement units; IEEE standard C37.118; power quality monitoring (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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