中国电机工程学报2011，Vol.31Issue(16)：74-80,7.
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
Application of UKF and SRUKF to Power System Dynamic State Estimation
摘要
Abstract
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 carried 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.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分类
信息技术与安全科学引用本文复制引用
卫志农,孙国强,庞博..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[J].中国电机工程学报,2011,31(16):74-80,7.基金项目
国家自然科学基金项目(50877024). （50877024）
