Estimation of the Dynamic States of Synchronous Machines Using an Extended Particle Filter

Author

Ning Zhou ; Da Meng ; Shuai Lu

Author_Institution

Pacific Northwest Nat. Lab., Richland, WA, USA

Volume

28

Issue

4

fYear

2013

fDate

Nov. 2013

Firstpage

4152

Lastpage

4161

Abstract

In this paper, an extended particle filter (PF) is proposed to estimate the dynamic states of a synchronous machine using phasor measurement unit (PMU) data. A PF propagates the mean and covariance of states via Monte Carlo simulation, is easy to implement, and can be directly applied to a nonlinear system with non-Gaussian noise. The proposed extended PF improves robustness of the basic PF through iterative sampling and inflation of particle dispersion. Using Monte Carlo simulations with practical noise and model uncertainty considerations, the extended PF´s performance is evaluated and compared with the basic PF, an extended Kalman filter (EKF) and an unscented Kalman filter (UKF). The extended PF results showed high accuracy and robustness against measurement and model noise.

Keywords

Monte Carlo methods; iterative methods; nonlinear filters; particle filtering (numerical methods); phasor measurement; state estimation; synchronous machines; EKF; Monte Carlo simulation; PMU data; UKF; dynamic state estimation; extended Kalman filter; extended particle filter; iterative sampling; model noise; model uncertainty; nonGaussian noise; nonlinear system; particle dispersion inflation; phasor measurement unit; synchronous machines; unscented Kalman filter; Extended Kalman filter (EKF); particle filter; phasor measurement unit (PMU); power system dynamics; state estimation; unscented Kalman filter (UKF);

fLanguage

English

Journal_Title

Power Systems, IEEE Transactions on

Publisher

ieee

ISSN

0885-8950

Type

jour

DOI

10.1109/TPWRS.2013.2262236

Filename

6529200