电力系统自动化2018，Vol.42Issue(10)：87-92,6.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
Improved Robust Unscented Kalman Filtering Algorithm for Dynamic State Estimation of Power Systems
曲正伟 1董一兵 1王云静 1陈亮2
作者信息
- 1. 电力电子节能与传动控制河北省重点实验室(燕山大学),河北省秦皇岛市066004
- 2. 国网河北省电力有限公司经济技术研究院,河北省石家庄市050024
- 折叠
摘要
Abstract
Aiming at the shortcomings of traditional unscented Kalman filter(UKF)sampling method in the dynamic state estimation,the UKF algorithm is improved by adj usting the ratio correction factor in real time to 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.The gross error criterion is introduced to detect the gross errors,and the enhancement factor is applied to reduce the influence of gross errors on system state estimation results.RUKF algorithm has been applied to the dynamic state estimation of the power system and the simulation results show that RUKF algorithm has good estimation performance and strong robustness.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Key words
unscented Kalman filter(UKF)/ratio correction factor/gross error/robustness/state estimation引用本文复制引用
曲正伟,董一兵,王云静,陈亮..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,2018,42(10):87-92,6.
