An extended Kalman particle filter for power system dynamic state estimation

Abstract

Purpose

The purpose of this paper is to propose an extended Kalman particle filter (EPF) approach for dynamic state estimation of synchronous machine using the phasor measurement unit’s measurements.

Design/methodology/approach

EPF combines the extended Kalman filter (EKF) with the particle filter (PF) to accurately estimate the dynamic states of synchronous machine. EKF is used to make particles of PF transfer to the likelihood distribution from the previous distribution. Therefore, the sample impoverishment in the implementation of PF is able to be avoided.

Findings

The proposed method is capable of estimating the dynamic states of synchronous machine with high accuracy. The real-time capability of this method is also acceptable.

Practical implications

The effectiveness of the proposed approach is tested on IEEE 30-bus system.

Originality/value

Introducing EKF into PF, EPF is proposed to estimate the dynamic states of synchronous machine. The accuracy of a dynamic state estimation is increased.

Citation

Yu, Y., Wang, Z. and Lu, C. (2018), "An extended Kalman particle filter for power system dynamic state estimation", COMPEL - The international journal for computation and mathematics in electrical and electronic engineering, Vol. 37 No. 6, pp. 1993-2005. https://doi.org/10.1108/COMPEL-11-2017-0493