Title :
A novel approach for ringdown detection using extended Kalman filter
Author :
Yazdanian, Masoud ; Mehrizi-Sani, Ali ; Mojiri, Mohsen
Author_Institution :
Sch. of Electr. Eng. & Comput. Sci., Washington State Univ., Pullman, WA, USA
Abstract :
Estimation of electromechanical modes has attracted attention during past few decades because the estimation of these modes provides vital information about the stability of the power system. In this paper, a new state-space model is developed for online detection of a ringdown signal using extended Kalman filter (EKF). The proposed model not only can estimate constant parameters, but it can also track time-varying parameters. Simulation results demonstrate the desirable performance of the proposed method for ringdown parameter estimation.
Keywords :
Kalman filters; power system stability; power system state estimation; EKF; electromechanical oscillations; extended Kalman filter; ringdown parameter estimation; ringdown signal online detection; time-varying parameters; Damping; Estimation; Frequency estimation; Kalman filters; Mathematical model; Noise; State-space methods; Extended Kalman filter; power system modes identification; ringdown detection;
Conference_Titel :
Industrial Electronics Society, IECON 2013 - 39th Annual Conference of the IEEE
Conference_Location :
Vienna
DOI :
10.1109/IECON.2013.6699652