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Extended modulating functions for simulation of wind velocities with weak and strong nonstationarity

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  • Li, Jinhua
  • Li, Chunxiang
  • He, Liang
  • Shen, Jianhong

Abstract

Following the theory of evolutionary power spectral density (EPSD) for nonstationary processes, it is anticipated that the nonstationary wind velocities can be generated through modulating the stationary wind velocities by resorting to desirable modulating functions. Naturally, the key to the simulation of the nonstationary wind velocities lies in seeking out desirable modulating functions. The purpose of this study thus is how to obtain the modulating functions suitable for modulating stationary wind velocities. This work begins with systematically deducing the modulating function according to Kaimal power spectrum. The modulating functions corresponding respectively to other power spectra are subsequently presented. Derived modulating functions herein are collectively referred to as the extended modulating functions (EMFs). Employing the recent research by Li et al. (2009) that integration of both the spline interpolation algorithm (SIA) and spectral representation (SR) method, then forming the SIA-SR method, is able to remarkably reduce the increasing number of Cholesky decomposition of the time-varying spectral density matrix, the simulation of the nonstationary wind velocities has been carried out with an EMF. It can be inferred in terms of simulation results that the EMFs can fully capture the nonstationarity of wind velocities, including both the weak and strong nonstationary wind velocities.

Suggested Citation

  • Li, Jinhua & Li, Chunxiang & He, Liang & Shen, Jianhong, 2015. "Extended modulating functions for simulation of wind velocities with weak and strong nonstationarity," Renewable Energy, Elsevier, vol. 83(C), pages 384-397.
    • Handle: RePEc:eee:renene:v:83:y:2015:i:c:p:384-397
    DOI: 10.1016/j.renene.2015.04.044
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    References listed on IDEAS

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    1. Aksoy, Hafzullah & Fuat Toprak, Z & Aytek, Ali & Erdem Ünal, N, 2004. "Stochastic generation of hourly mean wind speed data," Renewable Energy, Elsevier, vol. 29(14), pages 2111-2131.
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