Estimation of the Dynamic States of Synchronous Machines Using an Extended Particle Filter

被引：128

作者：

Zhou, Ning ^{[1
]}

Meng, Da ^{[1
]}

Lu, Shuai ^{[1
]}

机构：

[1] Pacific NW Natl Lab, Richland, WA 99352 USA

来源：

IEEE TRANSACTIONS ON POWER SYSTEMS | 2013年 / 28卷 / 04期

关键词：

Extended Kalman filter (EKF); particle filter; phasor measurement unit (PMU); power system dynamics; state estimation; unscented Kalman filter (UKF); STABILIZING CONTROL; POWER-SYSTEM;

D O I：

10.1109/TPWRS.2013.2262236

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：

In this paper, an extended particle filter (PF) is proposed to estimate the dynamic states of a synchronous machine using phasor measurement unit (PMU) data. A PF propagates the mean and covariance of states via Monte Carlo simulation, is easy to implement, and can be directly applied to a nonlinear system with non-Gaussian noise. The proposed extended PF improves robustness of the basic PF through iterative sampling and inflation of particle dispersion. Using Monte Carlo simulations with practical noise and model uncertainty considerations, the extended PF's performance is evaluated and compared with the basic PF, an extended Kalman filter (EKF) and an unscented Kalman filter (UKF). The extended PF results showed high accuracy and robustness against measurement and model noise.

引用

页码：4152 / 4161

页数：10

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