基于H∞扩展卡尔曼滤波器模型不确定性的动态状态估计  

Dynamic State Estimation with Model Uncertainties Using H∞Extended Kalman Filter

作　　者：范俊 FAN Jun(State Grid Hubei Jingzhou Power Supply Company,Jingzhou 434000,China)

机构地区：[1]国网湖北省电力有限公司荆州供电公司检修分公司,湖北荆州434000

出　　处：《通信电源技术》2020年第20期1-3,共3页Telecom Power Technology

摘　　要：当运用卡尔曼滤波器来跟踪系统动态状态变量时,一般假设系统动态参数模型是准确的。然而,由于电力系统动力学模型会受到各种不确定性的影响,如在不同运行条件下变化的发电机瞬态电抗、不确定的输入以及噪声统计数据等,因此该假设可能不成立,卡尔曼型滤波器的性能会大大降低。为限制这些参数不确定性的影响,提出了一种基于鲁棒控制理论的H∞扩展卡尔曼滤波器(H∞Extended Kalman Filter,HEKF)以及调整HEKF参数的方法,基于IEEE-39节点标准系统的仿真结果证明了HEKF的有效性。When implementing Kalman filters to track system dynamic state variables,the dynamical model is assumed to be accurate.However,this assumption may not hold true as power system dynamical model is subjected to various uncertainties,such as varying generator transient reactance in different operation conditions,uncertain inputs,or noise statistics.As a result,the performance of Kalman-type filters can be degraded significantly.To bound the influence of these uncertainties,this letter proposes an H∞Extended Kalman Filter(HEKF)based on the robust control theory.An approach to tune the parameter of HEKF is presented as well.Numerical results on the IEEE 39-bus system demonstrate the effectiveness of the HEKF.

关 键 词：动态状态估计 H∞滤波器 模型不确定性 扩展卡尔曼 

分 类 号：TN7[电子电信—电路与系统]