  This paper develops a robust extended Kalman filter to estimate the rotor
angles and the rotor speeds of synchronous generators of a multimachine power
system. Using a batch-mode regression form, the filter processes together
predicted state vector and PMU measurements to track the system dynamics faster
than the standard extended Kalman filter. Our proposed filter is based on a
robust GM-estimator that bounds the influence of vertical outliers and bad
leverage points, which are identified by means of the projection statistics.
Good statistical efficiency under the Gaussian distribution assumption of the
process and the observation noise is achieved thanks to the use of the Huber
cost function, which is minimized via the iteratively reweighted least squares
algorithm. The asymptotic covariance matrix of the state estimation error
vector is derived via the covariance matrix of the total influence function of
the GM-estimator. Simulations carried out on the IEEE 39-bus test system reveal
that our robust extended Kalman filter exhibits good tracking capabilities
under Gaussian process and observation noise while suppressing observation
outliers, even in position of leverage. These good performances are obtained
only under the validity of the linear approximation of the power system model.
