[1]王要强,杨志伟,王 义,等.计及噪声和模型参数不确定的发电机动态状态估计[J].郑州大学学报(工学版),2023,44(06):68-75.[doi:10.13705/j.issn.1671-6833.2023.06.006]
　WANG Yaoqiang,YANG Zhiwei,WANG Yi,et al.Dynamic State Estimation of Generators Considering Noise and Model Parameter Uncertainties[J].Journal of Zhengzhou University (Engineering Science),2023,44(06):68-75.[doi:10.13705/j.issn.1671-6833.2023.06.006]
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计及噪声和模型参数不确定的发电机动态状态估计()
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《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
44
期数:
2023年06期
页码:
68-75
栏目:
出版日期:
2023-12-25

文章信息/Info

Title:
Dynamic State Estimation of Generators Considering Noise and Model Parameter Uncertainties
作者:
王要强1; 2 ;  杨志伟1; 2 ;  王 义1; 2 ;  王克文1; 2 ;  梁 军1; 3
1. 郑州大学 电气与信息工程学院,河南 郑州 450001;2. 郑州大学 河南省电力电子与电力系统工程技术研究中 心,河南 郑州 450001;3. 卡迪夫大学,英国 卡迪夫 CF243AA
Author(s):
WANG Yaoqiang1; 2;  YANG Zhiwei1; 2;  WANG Yi1; 2 ;  WANG Kewen1; 2;  LIANG Jun1; 3
1. School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou 450001, China; 2. Henan Engineering Research Center of Power Electronics and Energy Systems, Zhengzhou University, Zhengzhou 450001, China; 3. Cardiff University, Cardiff CF243AA, U. K
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
Keywords:
generator;  dynamic state estimation;  H-infinity filter;  nonlinear filter;  particle filter;  model uncertain; ty;  non-Gaussian nois
分类号:
TM711
DOI:
10.13705/j.issn.1671-6833.2023.06.006
文献标志码:
A
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. 045 8,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。
Abstract:
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 Hinfinity 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. 045 8. 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

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相似文献/References:

[1]胡一德.一种性能完善的发电机相间短路保护的分析[J].郑州大学学报(工学版),1989,10(02):89.
　[J].Journal of Zhengzhou University (Engineering Science),1989,10(06):89.

更新日期/Last Update: 2023-10-22