摘要
针对当前电力系统动态状态估计主要采用的扩展卡尔曼滤波(EKF)法存在收敛速度慢、鲁棒性差的缺点,采用一种新的非线性方法——无迹卡尔曼滤波(UKF)法进行电力系统动态状态估计。UKF法由于使用了无迹变换,避免了线性化误差的引入和雅可比矩阵的计算,相比EKF法有更高的估计精度和稳定性。广域测量系统(WAMS)能够提供相量信息,具有精度高、全网严格同步等优点。因此,将WAMS量测数据和数据采集与监控(SCADA)系统量测数据相结合,形成应用混合量测的电力系统动态状态估计。仿真表明,UKF法相比EKF法能够更准确地估计动态系统中的状态量,WAMS信息的引入进一步提高了动态状态估计的性能。
Currently, the power system dynamic state estimation mainly adopts extended Kalman filter （EKF） method. Nevertheless, the EKF method normally suffers from slow convergence and low robustness. This paper introduces a new nonlinear method, namely unscented Kalman filter （UKF） method, to estimate the state of a power system. By applying the unscented transformation （UT）, the UKF method can avoid the error caused by the linearization process and the complicated calculation of Jacobian matrix in the EKF method, and therefore show better estimation accuracy and robustness than the EKF method. On the other hand, since wide area measurement system （WAMS） with its phasor measurements has advantages such as higher accuracy and network synchronization, the mixed measurement vector combining WAMS measurement and supervisory control and data acquisition system （SCADA） measurement is also introduced in the dynamic state estimation. The simulation shows that UKF method can estimate the state of power system more accurately than EKF method, and the introduction of WAMS can further improve the performance of the dynamic state estimation.
出处
《电力系统自动化》
EI
CSCD
北大核心
2010年第17期17-21,92,共6页
Automation of Electric Power Systems
基金
国家电网公司科技项目(SGKJ[2007]185)~~
关键词
动态状态估计
广域测量系统
电力系统
无迹卡尔曼滤波
扩展卡尔曼滤波
dynamic state estimation
wide area measurement system
power system
unscented Kalman filter
extended Kalman filter
