Dynamic State Estimation of a Synchronous Machine Using PMU Data: A Comparative Study

Accurate information about dynamic states is important for efficient control and operation of a power system. This paper compares the performance of four Bayesian-based filtering approaches in estimating dynamic states of a synchronous machine using phasor measurement unit data. The four methods are...

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Bibliographic Details
Published in:IEEE transactions on smart grid 2015-01, Vol.6 (1), p.450-460
Main Authors: Ning Zhou, Da Meng, Zhenyu Huang, Welch, Greg
Format: Article
Language:English
Subjects:
Data models
Ensemble Kalman filter (EnKF)
extended Kalman filter (EKF)
Kalman filters
Noise
particle filter (PF)
phasor measurement unit (PMU)
Phasor measurement units
Power system dynamics
State estimation
unscented Kalman filter (UKF)
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Summary:Accurate information about dynamic states is important for efficient control and operation of a power system. This paper compares the performance of four Bayesian-based filtering approaches in estimating dynamic states of a synchronous machine using phasor measurement unit data. The four methods are extended Kalman filter, unscented Kalman filter, ensemble Kalman filter, and particle filter. The statistical performance of each algorithm is compared using Monte Carlo methods and a two-area-four-machine test system. Under the statistical framework, robustness against measurement noise and process noise, sensitivity to sampling interval, and computation time are evaluated and compared for each approach. Based on the comparison, this paper makes some recommendations for the proper use of the methods.
ISSN:1949-3053
1949-3061
DOI:10.1109/TSG.2014.2345698