复杂噪声条件下基于抗差容积卡尔曼滤波的发电机动态状态估计  

Dynamic State Estimation of Synchronous Machines Based on Robust Cubature Kalman Filter under Complex Measurement Noise Conditions

作　　者：李扬[1] 李京 陈亮 李国庆[1] Li Yang;Li Jing;Chen Liang;Li Guoqing(School of Electrical Engineering,Northeast Electric Power University,Jilin 132012,China;State Grid Hebei Economic Research Institute,Shijiazhuang 050022,China)

机构地区：[1]东北电力大学电气工程学院,吉林132012 [2]国网河北省电力有限公司经济技术研究院,石家庄050022

出　　处：《电工技术学报》2019年第17期3651-3660,共10页Transactions of China Electrotechnical Society

基　　金：国家重点研发计划(2017YFB0902401);国家自然科学基金(51677023)资助

摘　　要：容积卡尔曼滤波(CKF)在非线性动态状态估计领域有着良好的估计效果。但由于容积卡尔曼滤波缺乏对量测噪声特性的在线自适应能力,其对不良数据和非高斯白噪声的处理效果并不理想。为了解决当量测量统计特性偏离先验统计特性时,容积卡尔曼滤波算法性能下降和发散的问题,通过将抗差估计理论中的M-估计理论与容积卡尔曼滤波相结合,提出抗差容积卡尔曼滤波(RCKF)算法,并将其尝试应用于复杂噪声条件下的发电机动态状态估计中。IEEE 9节点系统和新英格兰16机68节点系统的仿真结果表明:在不同量测噪声且量测量存在不良数据的复杂噪声条件下,与传统CKF算法相比,所提抗差CKF算法均具有更好的估计精度和收敛能力,并能有效消除不良数据对估计效果的影响。Cubature Kalman filter(CKF)has good performance when handling nonlinear dynamic state estimations.However,it cannot work well in non-Gaussian noise and bad data environment due to the lack of auto-adaptive ability to measure noise statistics on line.In order to address the problem of behavioral decline and divergence when measure noise statistics deviate prior noise statistics,a new robust CKF(RCKF)algorithm is developed by combining the Huber’s M-estimation theory with the classical CKF,and thereby it is proposed to coping with the dynamic state estimation of synchronous generators in this study.The simulation results on the IEEE-9 bus system and New England 16-machine-68-bus system demonstrate that the estimation accuracy and convergence of the proposed RCKF are superior to those of the classical CKF under complex measurement noise environments including different measurement noises and bad data,and that the RCKF is capable of effectively eliminating the impact of bad data on the estimation effects.

关 键 词：动态状态估计 发电机 容积卡尔曼滤波 M-估计理论 量测噪声分布 不良数据 相量测量单元数据 

分 类 号：TM71[电气工程—电力系统及自动化]