Online Estimation of the Mechanical Parameters of a Wind Turbine with Doubly Fed Induction Generator by Utilizing Turbulence Excitations

Yening Lai, Ling Zhu, Xueping Pan, Jinpeng Guo, Dazhuang He and Wei Liang
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Yening Lai: NARI Group Corporation (State Grid Electric Power Research Institute), Nanjing 211100, China
Ling Zhu: NARI Group Corporation (State Grid Electric Power Research Institute), Nanjing 211100, China
Xueping Pan: College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China
Jinpeng Guo: College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China
Dazhuang He: College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China
Wei Liang: College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China

Energies, 2022, vol. 15, issue 6, 1-14

Abstract: In this paper, a new method using wind turbulence excitation is proposed to estimate the parameters of the mechanical system (drivetrain and pitch angle controller) in a Doubly Fed Induction Generator (DFIG) Wind Turbine (WT). Firstly, simulations were carried out for a DFIG WT under turbulence excitations. The spectral contents of the responses imply that the transients of the electrical system (generator and converter), which are much faster than those of the mechanical system, can be neglected when estimating the mechanical parameters. Based on this, a simplified model related to the mechanical system of the DFIG WT was derived by applying the model reduction technique. Secondly, the parameter sensitivity of Power Spectral Density (PSD) was used to quantify the impacts of individual parameters on the dynamics of the mechanical system, and the influential parameters were selected on the basis of the sensitivity results. Finally, a weighted least-squares optimization problem, which is suitable for a system with close oscillation modes, was formulated for parameter estimation. The estimation results based on two different types of optimization methods were compared, and their estimation accuracies validate the effectiveness of the proposed method.

Keywords: DFIG; identifiability; parameter estimation; PSD sensitivity; weighted Levenberg–Marquardt method (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: 2022
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