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Investigation of Two Analytical Wake Models Using Data From Wind Farms

[+] Author Affiliations
Anshul Mittal, Lafayette K. Taylor, Kidambi Sreenivas, Abdollah Arabshahi

SimCenter - University of Tennessee, Chattanooga, TN

Paper No. IMECE2011-63286, pp. 1215-1222; 8 pages
doi:10.1115/IMECE2011-63286
From:
  • ASME 2011 International Mechanical Engineering Congress and Exposition
  • Volume 6: Fluids and Thermal Systems; Advances for Process Industries, Parts A and B
  • Denver, Colorado, USA, November 11–17, 2011
  • Conference Sponsors: ASME
  • ISBN: 978-0-7918-5492-1
  • Copyright © 2011 by ASME

abstract

A code ‘Wind Farm Optimization using a Genetic Algorithm’ (referred as WFOG) was developed for optimizing the placement of wind turbines in large wind farms. It utilizes an analytical wake model (by Jensen et al.) to minimize the cost per unit power for the wind farm. In this study, a new wake model by Ishihara et al. is tested in WFOG. The wake model takes into account the effect of atmospheric turbulence and rotor generated turbulence on the wake recovery. Results of the two wake models are compared with data from Horns Rev and Nysted wind farm. The maximum error (Horns Rev wind farm) for Ishihara’s wake model was 7% as compared to 15% for Jensen’s wake model. The optimal results obtained in earlier studies (using Jensen’s wake model) are compared to wind farm configurations obtained for Ishihara’s wake model. The optimization is carried out for the simplest wind regime: Constant wind speed and fixed wind direction.

Copyright © 2011 by ASME
Topics: Wakes , Wind farms

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