摘要
针对发电机动态状态估计过程中通信噪声以及模型参数不确定时估计精度降低和鲁棒性差的缺陷,提出了一种具有鲁棒性的发电机动态状态估计方法——H∞无迹粒子滤波(HUPF)。首先,建立四阶发电机的状态空间模型,利用无迹变换法计算粒子滤波的重要密度分布,提高了滤波精度和计算效率,增加了算法的灵活性;其次,根据H∞滤波理论建立发电机模型不确定性的边界约束准则,并在此基础上结合无迹粒子滤波(UPF),设计了一种可以根据模型不确定性动态调整估计误差协方差的更新策略,进一步提升了发电机的估计精度和抗差性能。通过IEEE 39节点系统中的仿真算例验证了所提方法的有效性,测试结果表明:所提HUPF方法的均方根误差最低为0.006,最高为0.0458,相比于UKF、UPF和AUKF方法,HUPF方法的均方根误差最小,能够显著提高模型不确定情形下发电机的状态估计精度,并且具有更强的鲁棒性。
In view of the defects of accuracy and robustness caused by the uncertainty of noise and model parameters in the process of generator dynamic state estimation,a robust dynamic state estimation method for generators—H-infinity unscented particle filter(HUPF)was proposed.Firstly,a fourth-order dynamic state space model of generator was established.Secondly,the uncertainty constraint criterion of model was constructed based on the H-infinity theory to define the uncertainty boundary range.By effectively combining robust control theory and particle filtering,and using unscented transformation to calculate the important density function,the particle swarm would be closer to the actual posterior probability distribution.Finally,a novel estimation error covariance update strategy was designed,which could be dynamically adjusted based on model uncertainty.In IEEE 39-bus system,the effectiveness of the proposed method was verified.The simulation results demonstrated that the minimum root mean square error(RMSE)of the proposed HUPF method was 0.006 and the maximum was 0.0458.Compared with UKF,UPF,and AUKF methods,the HUPF method had the smallest RMSE and could significantly improve the state estimation accuracy of the generator with model uncertainty and stronger robustness.
作者
王要强
杨志伟
王义
王克文
梁军
WANG Yaoqiang;YANG Zhiwei;WANG Yi;WANG Kewen;LIANG Jun(School of Electrical and Information Engineering,Zhengzhou University,Zhengzhou 450001,China;Henan Engineering Research Center of Power Electronics and Energy Systems,Zhengzhou University,Zhengzhou 450001,China;Cardiff University,Cardiff CF243AA,U.K.)
出处
《郑州大学学报（工学版）》
CAS
北大核心
2023年第6期68-75,共8页
Journal of Zhengzhou University（Engineering Science）
基金
国家自然科学基金资助项目(62203395,51507155)。
关键词
发电机
动态状态估计
H∞滤波
非线性滤波
粒子滤波
模型不确定性
非高斯噪声
generator
dynamic state estimation
H-infinity filter
nonlinear filter
particle filter
model uncertainty
non-Gaussian noise
