文章摘要
曲正伟,董一兵,王云静,等.用于电力系统动态状态估计的改进鲁棒无迹卡尔曼滤波算法[J].电力系统自动化. DOI: 10.7500/AEPS20170826003.
QU Zhengwei,DONG Yibing,WANG Yunjing, et al.An improved robust unscented Kalman filtering algorithm for dynamic state estimation of power systems[J].Automation of Electric Power Systems. DOI: 10.7500/AEPS20170826003.
用于电力系统动态状态估计的改进鲁棒无迹卡尔曼滤波算法
An improved robust unscented Kalman filtering algorithm for dynamic state estimation of power systems
DOI:10.7500/AEPS20170826003
关键词: 无迹卡尔曼滤波  比例修正因子  粗差  鲁棒性  状态估计
KeyWords: unscented Kalman filter  ratio correction factor  gross error  robustness  state estimation
上网日期:2018-04-11
基金项目:
作者单位E-mail
曲正伟 电力电子节能与传动控制河北省重点实验室(燕山大学) ysu_qzw@163.com 
董一兵 电力电子节能与传动控制重点河北省实验室(燕山大学) 827675941@qq.com 
王云静 电力电子节能与传动控制河北省重点实验室(燕山大学) ysuwyj@163.com 
陈亮 国网河北省电力有限公司经济技术研究院 ch.lg@163.com 
摘要:
      针对动态状态估计中传统UKF采样方法的不足,对UKF算法进行改进,每次估计时实时调节比例修正因子,提高滤波性能。动态状态估计结果精度受量测粗差影响较大,为此提出一种鲁棒无迹卡尔曼滤波(RUKF)算法,引入粗差判据检测粗差,通过增强因子来降低粗差对系统状态估计结果的影响。将RUKF算法运用到电力系统动态状态估计,仿真结果表明,该算法具有良好的估计性能,具有较强的鲁棒性。
Abstract:
      Aiming at the shortcomings of traditional UKF sampling method in dynamic state estimation,we improve the UKF algorithm,adjust the ratio correction factor in real time and improve the filtering performance.The accuracy of dynamic state estimation is greatly influenced by the gross error.Therefore, a robust unscented Kalman filter (RUKF) algorithm is proposed. Gross error criterion is introduced to detect gross errors,and the enhancement factor is applied to reduce the influence of gross errors on system state estimation results.Applied to the dynamic state estimation of the power system,the simulation results show that RUKF has good estimation performance and strong robustness.
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