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

    Authors

    IEEE Trans. Smart Grid

    Comments

    Authors: contact us about adding a copy of your work at STARS@ucf.edu

    Abbreviated Journal Title

    J. Appl. Phys.

    Keywords

    Ensemble Kalman filter (EnKF); extended Kalman filter (EKF); particle; filter (PF); phasor measurement unit (PMU); power system dynamics; state; estimation; unscented Kalman filter (UKF); POWER-SYSTEM; Engineering, Electrical & Electronic

    Abstract

    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.

    Subjects

    N. Zhou; D. Meng; Z. Y. Huang;G. Welch

    Volume

    6

    Issue/Number

    1

    Publication Date

    1-1-2014

    Document Type

    Article

    Language

    English

    First Page

    450

    Last Page

    460

    WOS Identifier

    WOS:000346731400045

    ISSN

    1949-3053

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