Dynamic state estimation in power systems using Kalman filters

Author

Tebianian, Hamed ; Jeyasurya, Benjamin

Author_Institution

Fac. of Eng. & Appl. Sci., Memorial Univ. of Newfoundland, St. John´s, NL, Canada

fYear

2013

fDate

21-23 Aug. 2013

Firstpage

1

Lastpage

5

Abstract

Dynamic state estimation of power systems is necessary for wide area control purposes. Among the states of synchronous machine, precise, accurate, and timely information about rotor angle and speed deviation can be useful to enhance power system reliability and stability. In this paper two popular nonlinear estimation approaches: Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) are used to estimate main states of a simple power system using high rate data provided by Phasor Measurement Unit (PMU). A case study using a simple power system model is presented to illustrate the effectiveness of proposed approaches.

Keywords

Kalman filters; nonlinear filters; phasor measurement; power system reliability; power system stability; power system state estimation; rotors; synchronous machines; EKF; PMU; UKF; dynamic state estimation; extended Kalman filter; nonlinear estimation approaches; phasor measurement unit; power system reliability; power system stability; rotor angle; speed deviation; synchronous machine; unscented Kalman filter; wide area control purposes; Equations; Kalman filters; Mathematical model; Phasor measurement units; Power system dynamics; Power system stability; State estimation; Extended Kalman Filter; Power system state estimation; Unscented Kalman Filter;

fLanguage

English

Publisher

ieee

Conference_Titel

Electrical Power & Energy Conference (EPEC), 2013 IEEE

Conference_Location

Halifax, NS

Print_ISBN

978-1-4799-0105-0

Type

conf

DOI

10.1109/EPEC.2013.6802979

Filename

6802979