Dynamic State Estimation of Power Systems by $p$ -Norm Nonlinear Kalman Filter

The problem of dynamic state estimation of power systems is relevant to the monitoring of real-time operation of essential power distribution infrastructure. The nonlinear Kalman filter is utilized for dynamic state estimation of power systems based on available measurements from phasor measurement units. However, measurements are corrupted by non-Gaussian noise and exhibit varying levels of sensitivity to outliers, therefore degrading estimation accuracy. This study proposes a robust mixed
$p$
-norm square root unscented Kalman filter for state estimation of power systems. Unlike traditional nonlinear Kalman filters which utilize the minimum mean square error criterion, the mixed
$p$
-norm square root unscented Kalman filter utilizes a mixed
$p$
-norm optimization for weighting the measurement errors to improve robustness against outliers and alleviate the filtering degradation caused by abnormal measurements. The performance of the
$p$
-norm square root unscented Kalman filter is demonstrated in the WSCC 3-machine system and the NPCC 48-machine system. Simulation results demonstrate that the
$p$
-norm square root unscented Kalman filter achieves superior accuracy than the commonly used nonlinear Kalman filters.