Particle Filter Approach to Dynamic State Estimation of Generators in Power Systems

被引：77

作者：

Emami, Kianoush ^{[1
]}

Fernando, Tyrone ^{[1
]}

Iu, Herbert Ho-Ching ^{[1
]}

Trinh, Hieu ^{[2
]}

Wong, Kit Po ^{[1
]}

机构：

[1] Univ Western Australia, Sch Elect Elect & Comp Engn, Crawley, WA 6009, Australia

[2] Deakin Univ, Sch Engn, Geelong, Vic 3217, Australia

来源：

IEEE TRANSACTIONS ON POWER SYSTEMS | 2015年 / 30卷 / 05期

关键词：

Energy management system; particle filter; power system dynamic estimation; unscented Kalman filter; wide area measurement system; KALMAN FILTER;

D O I：

10.1109/TPWRS.2014.2366196

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

This paper presents a novel particle filter based dynamic state estimation scheme for power systems where the states of all the generators are estimated. The proposed estimation scheme is decentralized in that each estimation module is independent from others and only uses local measurements. The particle filter implementation makes the proposed scheme numerically simple to implement. What makes this method superior to the previous methods which are mainly based on the Kalman filtering technique is that the estimation can still remain smooth and accurate in the presence of noise with unknown changes in covariance values. Moreover, this scheme can be applied to dynamic systems and noise with both Gaussian and non-Gaussian distributions.

引用

页码：2665 / 2675

页数：11

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