Title

A Two-Stage Kalman Filter Approach for Robust and Real-Time Power System State Estimation

Authors

Authors

IEEE Trans. Sustain. Energy

Comments

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

Abbreviated Journal Title

J. Am. Hist.

Keywords

Adaptive Kalman filter; bad data processing; phasor measurement units; (PMUs); power system dynamic state; robust state estimation; Energy & Fuels; Engineering, Electrical & Electronic

Abstract

As electricity demand continues to grow and renewable energy increases its penetration in the power grid, real-time state estimation becomes essential for system monitoring and control. Recent development in phasor technology makes it possible with high-speed time-synchronized data provided by phasor measurement units (PMUs). In this paper, we present a two-stage Kalman filter approach to estimate the static state of voltage magnitudes and phase angles, as well as the dynamic state of generator rotor angles and speeds. Kalman filters achieve optimal performance only when the system noise characteristics have known statistical properties (zero-mean, Gaussian, and spectrally white). However, in practice, the process and measurement noise models are usually difficult to obtain. Thus, we have developed the adaptive Kalman filter with inflatable noise variances (AKF with InNoVa), an algorithm that can efficiently identify and reduce the impact of incorrect system modeling and/or erroneous measurements. In stage one, we estimate the static state from raw PMU measurements using the AKF with InNoVa; then in stage two, the estimated static state is fed into an extended Kalman filter to estimate the dynamic state. The simulations demonstrate its robustness to sudden changes of system dynamics and erroneous measurements.

Subjects

J. H. Zhang; G. Welch; G. Bishop;Z. Y. Huang

Volume

5

Issue/Number

2

Publication Date

1-1-2014

Document Type

Article

Language

English

First Page

629

Last Page

636

WOS Identifier

WOS:000333287200030

ISSN

1949-3029

Share

COinS