Title :
Enhancing Kalman Filter for Tracking Ringdown Electromechanical Oscillations
Author :
Peng, Jimmy Chih-Hsien ; Nair, Nirmal-Kumar C.
Author_Institution :
Dept. of Electr. & Comput. Eng., Univ. of Auckland, Auckland, New Zealand
fDate :
5/1/2012 12:00:00 AM
Abstract :
Ringdown detection methods like Kalman filter and Prony analysis have been developed to aid transmission operators to track lightly-damped inter-area oscillations. Kalman filter detection is dependent on a priori system knowledge and is designed to monitor the dominant mode. The objective of this paper is to extend Kalman filter to track multiple modes. Thus, extended complex Kalman filter (ECKF) based procedure is formulated and assessed. While retaining the recursive Kalman filter engine, the proposed method redefines the state variable representation to directly estimate the modal parameters instead of using the existing polynomial rooting approach. It also integrates Hankel singular value decomposition (HSVD) to provide better estimates of the initial conditions.
Keywords :
Kalman filters; Monte Carlo methods; phasor measurement; polynomials; power system stability; recursive filters; signal detection; singular value decomposition; ECKF based procedure; HSVD; Hankel singular value decomposition; Kalman filter enhancement; Prony analysis; exhaustive Monte Carlo simulations; extended complex Kalman filter based procedure; lightly-damped interarea oscillation tracking; phasor measurement unit; polynomial rooting approach; recursive Kalman filter engine; ringdown detection methods; ringdown electromechanical oscillation tracking; transmission operators; Accuracy; Damping; Kalman filters; Monitoring; Noise; Oscillators; Phasor measurement units; Extended complex Kalman filter; Kalman filter; phasor measurement unit; power oscillations; ringdown detection; synchrophasor measurements;
Journal_Title :
Power Systems, IEEE Transactions on
DOI :
10.1109/TPWRS.2011.2169284