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Local sequential ensemble Kalman filter for simultaneously tracking states and parameters

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4 Author(s)
Ning Zhou ; Energy and Environment Directorate, Pacific Northwest National Laboratory, Richland, WA 99352, USA ; Zhenyu Huang ; Yulan Li ; Greg Welch

Accurate information about dynamic states and parameters is important for efficient control and operation of a power system. To improve the estimation accuracy of states and parameters, this paper applies a local sequential ensemble Kalman filter (EnKF) method to simultaneously estimate dynamic states and parameters using phasor-measurement-unit (PMU) data. Based on simulation studies using multi-machine systems, the proposed method performed favorably in tracking both states and parameters in real time.

Published in:

North American Power Symposium (NAPS), 2012

Date of Conference:

9-11 Sept. 2012