A Two-stage Kalman Filtering Approach for Robust and Real-time Power Systems State Tracking

As electricity demand continues to grow and renewable energy increases its penetration in the power grid, realtime state tracking becomes essential for system monitoring and control. Recent developments in phasor technology make realtime dynamic state estimation possible with high-speed timesynchronized data provided by synchronized Phasor Measurement Units (PMU). In this paper we present a two-stage Kalman filtering approach to estimate the static states of voltage magnitudes and phase angles, as well as the dynamic states of generator rotor angles and generator 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 in the first stage, we estimate the static states from raw PMU measurements, using a lightweight but efficient adaptive Kalman filtering algorithm called Adaptive Kalman Filter with Inflatable Noise Variances (AKF with InNoVa), which can identify and reduce the impact of incorrect system modeling and/or bad PMU measurements. In the next stage, the estimated bus voltages are fed into an extended Kalman filter to obtain the dynamic state estimations. Simulations demonstrate its robustness to sudden changes of system dynamics and erroneous PMU measurements.