Dynamic state estimation of electromechanical transient for generators based on particle filter algorithm

被引：0
|
作者：
Cai G. [1 ]
Zheng C. [1 ]
Yang D. [1 ]
Sun Z. [1 ]
Wang Y. [1 ]
Yang Z. [1 ]
机构：
[1] School of Electrical Engineering, Northeast Dianli University, Jilin
来源：
Dianli Xitong Zidonghua/Automation of Electric Power Systems | 2016年 / 40卷 / 02期
基金：
中国国家自然科学基金;
关键词：
Electromechanical transient; Particle filter; Path correlation measure; State estimation;
D O I：
10.7500/AEPS20150109010
中图分类号：
学科分类号：
摘要：
As a kind of measuring method, wide area measurement system (WAMS) inevitably has measuring errors. In order to obtain a better control strategy and more accurate analysis results, it is necessary to do filtering processing for the actual measured data before application. A new method of dynamic filtering estimation for the actual measured data is proposed, and a dynamic state estimating model of a generator is established based on the second-order dynamic equation. Considering the nonlinearity of the model, the particle filtering (PF) with the unique advantage of dealing with non-linear and non-Gaussian stochastic system estimation problems is also used. To solve the problems of computation taking up space, large amounts of calculation and sample degradation, sequential importance resampling (SIR) is introduced on the basis of PF algorithm. Meanwhile, extended Kalman filter (EKF) is also adopted to do the state estimation, with the results compared with those using the approach proposed. Besides, to measure the estimation effect quantitatively, an evaluation index system based on the estimate path similarity is set. Finally, through the simulation calculation of a CEPRI 7-bus system, it is shown that the estimation based on the PF has a higher correlation with the actual result and a smaller error compared with the root mean square of the real value, a result better than the estimation based on the EKF and effectively reducing the influence of erroneous data. © 2016, Automation of Electric Power Systems Press.
引用
收藏
页码：49 / 54
页数：5
相关论文
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