Modeling and Identification of Wind Power Generation System Based on Hammerstein Model

doi:10.16182/j.issn1004731x.joss.22-0246

Abstract

Abstract:

A modeling and identification method of wind power generation system based on Hammerstein model is studied to establish high-precision model of wind power generation system. Firstly, 3σ criterion is used to propose the abnormal data, and the eliminated data is used to train the nominal model of the wind power generation system. Furthermore, the Hammerstein model is used to establish the data-driven model of wind power generation system, and the combined signal composed of separable signal and actual wind speed is used as the input of the Hammerstein model. The output of the separable signal through the nominal model and the actual power are used as the output of the Hammerstein model. Based on the input and output data of the combined signal, the parameters of static nonlinear subsystem and dynamic linear subsystem in the Hammerstein model are identified by correlation analysis and recursive extended stochastic gradient method. Simulation experiments with actual wind speed data show that the mean absolute percentage errors of the proposed method and the augmented stochastic gradient method are 4.99% and 14.73%, respectively. Compared with the extended stochastic gradient method, the average absolute percentage error of the proposed method is reduced by 9.74%. The simulation results show that the proposed method can effectively identify the Hammerstein model wind power generation system and has good prediction performance.

Key words: wind power generation system, Hammerstein model, combined signal, two-stage identification

CLC Number:

TP11

Feng Li, Tian Zheng, Wei Song. Modeling and Identification of Wind Power Generation System Based on Hammerstein Model[J]. Journal of System Simulation, 2023, 35(7): 1517-1525.

Figures/Tables 9

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