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Multivariable wind modeling in state space

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  • Teimouri-Sichani, M.
  • Pedersen, B.J.

Abstract

Turbulence of the incoming wind field is of paramount importance to the dynamic response of wind turbines. Hence reliable stochastic models of the turbulence should be available from which time series can be generated for dynamic response and structural safety analysis. In the paper an empirical cross-spectral density function for the along-wind turbulence component over the rotor plane is taken as the starting point. The spectrum is spatially discretized in terms of a Hermitian cross-spectral density matrix for the turbulence state vector which turns out not to be positive definite. Since the succeeding state space and ARMA modeling of the turbulence rely on the positive definiteness of the cross-spectral density matrix, the problem with the non-positive definiteness of such matrices is at first addressed and suitable treatments regarding it are proposed. From the adjusted positive definite cross-spectral density matrix a frequency response matrix is constructed which determines the turbulence vector as a linear filtration of Gaussian white noise. Finally, an accurate state space modeling method is proposed which allows selection of an appropriate model order, and estimation of a state space model for the vector turbulence process incorporating its phase spectrum in one stage, and its results are compared with a conventional ARMA modeling method.

Suggested Citation

  • Teimouri-Sichani, M. & Pedersen, B.J., 2011. "Multivariable wind modeling in state space," Renewable Energy, Elsevier, vol. 36(7), pages 1964-1972.
    • Handle: RePEc:eee:renene:v:36:y:2011:i:7:p:1964-1972
    DOI: 10.1016/j.renene.2010.12.014
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    References listed on IDEAS

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    1. Kavasseri, Rajesh G. & Seetharaman, Krithika, 2009. "Day-ahead wind speed forecasting using f-ARIMA models," Renewable Energy, Elsevier, vol. 34(5), pages 1388-1393.
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