摘要
动态状态估计对于电力系统的监测和稳定性分析具有重要意义。相量测量单元（phasor measurement unit,PMU）具备高采样率和同步数据,在动态状态估计中得到了广泛的应用。然而,由于PMU量测数据存在随机噪声,无法直接作为调度和控制的参考数据。基于此,该文提出一种基于插值H∞扩展卡尔曼滤波（interpolation H∞extended Kalman filter, IHEKF）的发电机动态状态估计方法。该方法在扩展卡尔曼滤波（extended Kalman filter,EKF）的基础上,利用自适应插值技术提高估计精度,并进一步采用H∞理论提高对噪声的鲁棒性。算例结果表明,IHEKF无论是在估计精度上还是在对噪声的鲁棒性能上较EKF均有所提高。
Dynamic state estimation is essential for monitoring and analyzing power system stability. With high sampling rates and well synchronized data, phasor measurement unit （PMU） has been widely used in dynamic state estimation （DSE）. However, the PMU data cannot be used directly by controlling and scheduling due to the stochastic noise. Based on interpolation H∞ extended Kalman filter （IHEKF）, in this paper, a novel dynamic state estimation for synchronous machines was proposed. On the basis of the extended Kalman filter （EKF）, by using the adaptive interpolation method and the H∞ theory, the accuracy of estimation and the robustness to measurement noise had been improved. Finally, simulation results show that the IHEKF performs well in the estimation accuracy, as well as the robustness to measurement noise, compared with the EKF.
作者
艾蔓桐
孙永辉
王义
卫志农
孙国强
AI Mantong;SUN Yonghui;WANG Yi;WEI Zhinong;SUN Guoqiang(Energy and Electrical College,Hohai University,Nanjing 210098,Jiangsu Province,China)
出处
《中国电机工程学报》
EI
CSCD
北大核心
2018年第19期5846-5853,共8页
Proceedings of the CSEE
基金
国家自然科学基金项目(61673161)
江苏省自然科学基金项目(BK20161510)
中央高校基本科研业务费项目(2017B13914)~~
关键词
动态状态估计
发电机
插值H∞扩展卡尔曼滤波
非线性
鲁棒性
dynamic state estimation （DSE）
synchronousmachine
interpolation H∞ extended Kalman filter （IHEKF）
nonlinearity
robustness
