A robust extended Kalman filter for power system dynamic state estimation using PMU measurements

TL;DR

This paper introduces a robust extended Kalman filter for power system dynamic state estimation that effectively handles outliers and bad data in PMU measurements, improving accuracy and robustness.

Contribution

A novel robust extended Kalman filter based on GM-estimator and projection statistics for improved power system state estimation.

Findings

Effective outlier suppression in PMU data

Maintains accuracy under Gaussian noise conditions

Demonstrates superior tracking on IEEE 39-bus system

Abstract

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.