[1]江辉,谢兴,王志忠,等.基于优化无迹Kalman滤波的电网动态谐波估计[J].深圳大学学报理工版,2015,32(2):188-195.[doi:10.3724/SP.J.1249.2015.02188]
 Jiang Hui,Xie Xing,Wang Zhizhong,et al.Dynamic harmonic estimation based on optimized unscented Kalman filter model[J].Journal of Shenzhen University Science and Engineering,2015,32(2):188-195.[doi:10.3724/SP.J.1249.2015.02188]
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基于优化无迹Kalman滤波的电网动态谐波估计()
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《深圳大学学报理工版》[ISSN:1000-2618/CN:44-1401/N]

卷:
第32卷
期数:
2015年第2期
页码:
188-195
栏目:
电子与信息科学
出版日期:
2015-03-20

文章信息/Info

Title:
Dynamic harmonic estimation based on optimized unscented Kalman filter model
文章编号:
201502011
作者:
江辉1谢兴1王志忠1彭建春2
1)深圳大学光电工程学院,深圳 518060
2)深圳大学机电与控制工程学院,深圳 518060
Author(s):
Jiang Hui1 Xie Xing1 Wang Zhizhong1 and Peng Jianchun2
1) College of Optoelectronic Engineering ,Shenzhen University, Shenzhen 518060,P.R.China
2) College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen 518060, P.R.China
关键词:
电力系统电能质量动态谐波估计无迹卡尔曼滤波粒子群算法状态噪声协方差观测噪声协方差
Keywords:
power system power quality dynamic harmonic estimation unscented Kalman filter particle swarm optimization state noise covariance measurement noise covariance
分类号:
TM 711;TM 93
DOI:
10.3724/SP.J.1249.2015.02188
文献标志码:
A
摘要:
提出一种基于粒子群优化的无迹卡尔曼滤波(particle swarm optimized unscented Kalman filter, PSOUKF)的电网动态谐波估计方法,利用包含种群分类与动态学习因子的改进粒子群优化算法,优化无迹卡尔曼滤波算法(unscented Kalman filter,UKF)的状态噪声协方差和观测噪声协方差,使系统噪声对电网动态谐波估计结果的影响得到充分考虑,克服了传统UKF算法将这两种方差视为常数导致的动态谐波估计精度低的缺陷. 仿真结果表明,PSOKUF算法比卡尔曼滤波(Kalman filter,KF)算法和传统的UKF算法更有效,在没有增加计算复杂度的情况下,能够提高动态谐波估计精度.
Abstract:
We propose a particle swarm optimized unscented Kalman filter (PSOUKF) method to estimate the power system dynamic harmonics. By using the improved particle swarm optimization algorithm with species classification and dynamic learning factor, we optimize the state noise covariance and the measurement noise covariance of the unscented Kalman filter (UKF) so as to sufficiently take the impacts of power system noise on dynamic harmonic estimation into account. The proposed method overcomes the deficiency of low dynamic harmonic estimation accuracy in the traditional UKF method in which the above two kinds of covariance are taken as constants. Simulation results show that the proposed PSOUKF is more effective than Kalman filter (KF) and UKF, and PSOUKF can improve the dynamic harmonic estimation accuracy without increasing the computational complexity.

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备注/Memo

备注/Memo:
基金项目:国家自然科学基金资助项目(51177102);深圳市基础研究计划项目(JCYJ20140418193546100, JCYJ20120817164050203)
作者简介:江辉(1968—),女(汉族),湖南省常德市人,深圳大学教授.E-mail: huijiang@szu.edu.cn
引文:江辉,谢兴 ,王志忠,等.基于优化无迹Kalman滤波的电网谐波估计[J]. 深圳大学学报理工版,2015,32(2):188-195.
Received:2014-10-04;Accepted:2014-12-29
Foundation:National Natural Science Technology of China (51177102);Shenzhen Science and Technology Research Foundation for Basic Project (JCYJ20140418193546100,JCYJ20120817164050203)
Corresponding author:Professor Jiang Hui.E-mail: huijiang@szu.edu.cn
Citation:Jiang Hui,Xie Xing,Wang Zhizhong,et al. Dynamic harmonic estimation based on optimized unscented Kalman filter model[J]. Journal of Shenzhen University Science and Engineering, 2015, 32(2): 188-195.(in Chinese)
更新日期/Last Update: 2015-03-12