Robust Dynamic State Estimation of Power System With Measurement Outliers Based on Parameterized Analytical Cubature Kalman Filter

Abstract

ABSTRACT Accurate state estimation is paramount for the smooth operation and management of power systems, significantly contributing to their safety, stability, and reliability. However, the presence of channel noise and outliers stemming from phasor measurement units renders as the noise model a deviation from the Gaussian distribution. To mitigate this challenge, this paper introduces a parameterized analytical update cubature Kalman filter (PACKF) that significantly enhances estimation accuracy. Firstly, the updated analytical form of the state variable is derived, in which an unknown parameter is introduced. Secondly, the unknown parameter is approximated using fixed‐point iteration, followed by the analytical computation of the required joint posterior probability density function (PDF). Finally, extensive simulations are conducted on the IEEE 39‐bus test system, indicating that the proposed method commendable accuracy and efficiency across diverse scenarios.