摘要
模型不确定性会影响状态估计的精度,甚至造成严重的后果。针对该问题,提出一种计及模型不确定性的发电机动态状态估计新方法。首先,基于发电机的动态方程建立发电机动态状态估计模型,进而提出基于自适应H_∞扩展卡尔曼滤波(AHEKF)的动态状态估计方法,该方法依据H_∞滤波理论建立模型不确定性约束准则,采用自适应技术对预测误差协方差矩阵和系统噪声协方差矩阵进行动态在线调整,从而使其具有较高的估计精度和鲁棒性。最后,通过WSCC 3机9节点系统和某实际大区域电网系统的算例测试,将所提方法与H_∞扩展卡尔曼滤波(HEKF)算法及扩展卡尔曼滤波(EKF)算法性能进行对比。算例结果表明,AHEKF算法在估计精度及鲁棒性方面均优于HEKF和EKF算法。
It is known that the model uncertainties will affect the accuracy of state estimation results,which even bring serious consequences.A novel method is proposed for dynamic state estimation of generators with model uncertainties.Firstly,the dynamic state estimation model is developed based on the dynamic equations of generators,and then the dynamic state estimation method based on the adaptive H_∞ extended Kalman filter（AHEKF）is proposed.In this method,based on the H∞ filtering theory,the constraint criterion of model uncertainty is established,and the adaptive technique is used to dynamically adjust the covariance matrix and process noise covariance matrix,therefore it has high estimation precision and robustness.Finally,the performance of the proposed method is compared with H∞ extended Kalman filter（HEKF）algorithm and extended Kalman filter（EKF）algorithm both in WSCC 3-machine 9-bus system and an actual large regional power grid system.Simulation results show that,compared with HEKF and EKF algorithm,the AHEKF algorithm performs better in estimation precision and robustness to model uncertainties.
作者
王义
孙永辉
钟永洁
卫志农
孙国强
WANG Yi;SUN Yonghui;ZHONG Yongjie;WEI Zhinong;SUN Guoqiang(College of Energy and Electrical Engineering,Hohai University,Nanjing 210098,China)
出处
《电力系统自动化》
EI
CSCD
北大核心
2018年第21期77-83,共7页
Automation of Electric Power Systems
基金
国家自然科学基金资助项目(61673161)
江苏省自然科学基金资助项目(BK20161510)
中央高校基本科研业务费专项资金资助项目(2017B13914)~~
关键词
模型不确定性
动态估计
自适应H∞扩展卡尔曼滤波
鲁棒性
model uncertainties
dynamic estimation
adaptive H∞ extended Kalman filter （AHEKF）
robustness
