An extended Kalman particle filter for power system dynamic state estimation. Issue 6 (2nd October 2018)

• Record Type: Journal Article
• Authors: Yu, Yang; Wang, Zhongjie; Lu, Chengchao
• Is Part Of: Compel. Volume 37:Issue 6(2018)
• Page Start: 1993
• Page End: 2005
• Publication Date: 2018-10-02
• Subjects: Dynamic state estimation -- Extended Kalman filter -- Particle filter -- Power system; Electrical engineering -- Data Processing -- Periodicals; Electrical engineering -- Mathematics -- Periodicals; Electrical engineering -- Periodicals; Electronics -- Data Processing -- Periodicals; Electronics -- Mathematics -- Periodicals; 621.3
• DOI: 10.1108/COMPEL-11-2017-0493
• Languages: English
• ISSNs: 0332-1649

Abstract

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.