Comparative Analysis of Identification Methods for Mechanical Dynamics of Large-Scale Wind Turbine

Jingchun Chu, Ling Yuan, Yang Hu, Chenyang Pan and Lei Pan
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Jingchun Chu: Guodian United Power Technology Company Limited, Beijing 102209, China
Ling Yuan: Guodian United Power Technology Company Limited, Beijing 102209, China
Yang Hu: School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China
Chenyang Pan: School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China
Lei Pan: Guodian United Power Technology Company Limited, Beijing 102209, China

Energies, 2019, vol. 12, issue 18, 1-24

Abstract: With increasing size and flexibility of modern grid-connected wind turbines, advanced control algorithms are urgently needed, especially for multi-degree-of-freedom control of blade pitches and sizable rotor. However, complex dynamics of wind turbines are difficult to be modeled in a simplified state-space form for advanced control design considering stability. In this paper, grey-box parameter identification of critical mechanical models is systematically studied without excitation experiment, and applicabilities of different methods are compared from views of control design. Firstly, through mechanism analysis, the Hammerstein structure is adopted for mechanical-side modeling of wind turbines. Under closed-loop control across the whole wind speed range, structural identifiability of the drive-train model is analyzed in qualitation. Then, mutual information calculation among identified variables is used to quantitatively reveal the relationship between identification accuracy and variables’ relevance. Then, the methods such as subspace identification, recursive least square identification and optimal identification are compared for a two-mass model and tower model. At last, through the high-fidelity simulation demo of a 2 MW wind turbine in the GH Bladed software, multivariable datasets are produced for studying. The results show that the Hammerstein structure is effective for simplify the modeling process where closed-loop identification of a two-mass model without excitation experiment is feasible. Meanwhile, it is found that variables’ relevance has obvious influence on identification accuracy where mutual information is a good indicator. Higher mutual information often yields better accuracy. Additionally, three identification methods have diverse performance levels, showing their application potentials for different control design algorithms. In contrast, grey-box optimal parameter identification is the most promising for advanced control design considering stability, although its simplified representation of complex mechanical dynamics needs additional dynamic compensation which will be studied in future.

Keywords: wind turbine; dynamic modeling; grey-box parameter identification; subspace identification; recursive least squares; optimal identification (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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