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Knowledge of the process noise covariance matrix Q is essential for the application of Kalman filtering. However, it is usually a difficult task to obtain an explicit expression of Q for large time varying systems. This paper looks at an adaptive Kalman filter method for dynamic harmonic state estimation and harmonic injection tracking. The method models the system as a linear frequency independent state model and does not require an exact knowledge of the noise covariance matrix Q. As an alternative, the proposed adaptive Kalman filter switches between the two basic Q models for steady-state and transient estimation. Its adaptive function allows for the resetting of the Kalman gain to avoid Kalman filter divergence problems under steady-state and allow fast tracking of system variations in transient conditions. Simulation results on the 220 kV network of the lower South Island of New Zealand are presented to validate this approach.
Date of Publication: April 2005