期刊文献+

无迹卡尔曼滤波及其平方根形式在电力系统动态状态估计中的应用 被引量:46

Application of UKF and SRUKF to Power System Dynamic State Estimation
原文传递
导出
摘要 针对扩展卡尔曼滤波(extended Kalman filter,EKF)的不足,将不需要对非线性系统函数进行线性化的无迹卡尔曼滤波(unscented Kalman filter,UKF)方法引入电力系统动态状态估计,采用生成Sigma点数量最少的比例最小偏度单形采样策略进行无迹变换。以IEEE 14系统为算例,仿真结果表明引入UKF后,估计结果的精度有所提高,但算法的效率较低,且数值稳定性较差。进一步引入平方根形式的UKF(square root UKF,SRUKF)模型,IEEE 14及IEEE 30测试系统的仿真结果证明:在不需要大量牺牲计算时间的同时,算法的数值稳定性得到了改善。表明SRUKF的引入对动态状态估计方法的改进是有效的。 Aiming at the shortcomings of the extended Kalman filter (EKF), the unscented Kalman filter (UKF), which avoids the linearization of the nonlinear system function, is introduced into power system dynamic state estimation. The sampling strategy called scale-corrected minimal skew simplex sampling is adapted so the least Sigma points are generated in the unscented transform process. For the IEEE 14-bus system, the estimation accuracy is improved, while the efficiency is lower and the numerical stability is poorer than EKF. Then, the square root UKF (SRUKF) is introduced. Simulations are carded out for the IEEE 14-bus system and IEEE 30-bus system, which show that the calculating time is saved and the numerical stability is improved. The introduction of SRUKF model is effective for improving dynamic state estimation approach.
出处 《中国电机工程学报》 EI CSCD 北大核心 2011年第16期74-80,共7页 Proceedings of the CSEE
基金 国家自然科学基金项目(50877024)~~
关键词 电力系统 动态状态估计 扩展卡尔曼滤波 无迹 卡尔曼滤波 平方根形式的无迹卡尔曼滤波 power system dynamic state estimation extended Kalman filter (EKF) tmscented Kalman filter (UKF) square root UKF (SRUKF)
  • 相关文献

参考文献26

  • 1DebsA S, Larson R E. A dynamic estimator for tracking the state of a power system[J]. IEEE Transactions on Power Apparatus arid Systems, 1970, 89(7): 1670-1678.
  • 2Mandal J K, SinhaAK. Incorporating nonlinearities of measurement function in power system dynamic state estimation[J]. IEE Processing: Generation, Transmission, Distribution, 1995, 142(2): 289-296.
  • 3Lin J M, Huang S J. Application of sliding surface- enhanced fuzzy control for dynamic state estimation of a power system[J]. IEEE Transactions on Power Systems, 2003, 18(2): 570-577.
  • 4张伯明,王世缨,相年德.电力系统实时运行状态的估计和预报[J].中国电机工程学报,1991,11(S1):70-76. 被引量:24
  • 5Shih K R, Huang S J. Application of a robust algorithm for dynamic state estimation of a power system[J]. IEEE Transactions on Power Systems, 2002, 17(1): 141-147.
  • 6Sinha A K, Mandal J K. Dynamic state estimation using ANN based bus load prediction[J]. IEEE Transactions on Power Systems, 1999, 14(11): 1219-1225.
  • 7Julier S J, Uhlmann J K, Durrant-Whyte H F. A new approach for filtering nonlinear systems[C]//Proceedings of the American Control Conference. Seattle, Washington: IEEE, 1995(3): 1628-1632.
  • 8Wan E A, de Mervwe R V. The unscented Kalman filter in Kalman filtering and neural networks[EB/OL]. 2004-03-1012010-03-20]. http://www.cse.ogi.edu/PacSo ft/proj e-cts/sec/wan01 b.ps.
  • 9余佩琼,陈子辰.基于UKF的永磁直线电机进给系统位置与速度估计[J].电工技术学报,2007,22(9):56-61. 被引量:4
  • 10李洁,钟彦儒.基于无轨迹卡尔曼滤波器的感应电机转速估计[J].系统仿真学报,2006,18(3):693-697. 被引量:8

二级参考文献57

共引文献68

同被引文献466

引证文献46

二级引证文献408

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部