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- Title
计及噪声和模型参数不确定的发电机动态状态估计.
- Authors
王要强; 杨志伟; 王 义; 王克文; 梁 军
- 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.
- Publication
Journal of Zhengzhou University: Engineering Science, 2023, Vol 44, Issue 5, p68
- ISSN
1671-6833
- Publication type
Article
- DOI
10.13705/j.issn.1671-6833.2023.06.006