Summary of Currently Used Wind Turbine Performance Prediction Computer Codes

Perkins, F. W.

doi:10.2172/6269779

Cited by 2 publications

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“…• restrained C/, max tip airfoils for peak power control with optimum blade radius, increased annual energy production, and lower wind farm array losses; • reduced roughness sensitivity for improved energy capture under dirty blade conditions; (Wilson and Lissaman, 1974;Wilson and Walker, 1974;Wilson et al, 1976;Perkins, 1979), and after various improvements (Hibbs and Rudkey, 1983;Tangier, 1983Tangier, , 1987, it has become an industry standard. Desirable features of the code are that it allows for rapid analysis, accommodates different airfoil data for each blade element, and includes a three-dimensional post-stall airfoil performance synthesization method for better peak power prediction at high wind speed Naik, 1984, 1985;Viterna and Corrigan, 1981).…”

Section: Applicationmentioning

confidence: 99%

Application of a Genetic Algorithm to Wind Turbine Design

Selig¹,

Coverstone-Carroll

1996

Journal of Energy Resources Technology

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This paper presents an optimization method for stall-regulated horizontal-axis wind turbines. A hybrid approach is used that combines the advantages of a genetic algorithm with an inverse design method. This method is used to determine the optimum blade pitch and blade chord and twist distributions that maximize the annual energy production. To illustrate the method, a family of 25 wind turbines was designed to examine the sensitivity of annual energy production to changes in the rotor blade length and peak rotor power. Trends are revealed that should aid in the design of new rotors for existing turbines. In the second application, five wind turbines were designed to determine the benefits of specifically tailoring wind turbine blades for the average wind speed at a particular site. The results have important practical implications related to rotors designed for the Midwestern US versus those where the average wind speed may be greater. The Design MetiiodologyGenetic Algorithm. As background, genetic algorithms were first developed by John Holland and his students at the University of Michigan in the 1970s. Since their conception, GAs have gained in popularity and have been used to solve optimization problems in many diverse subjects, such as engineering, science, economics, and finance. Very recently, Holland (1992) proved theoretically and empirically that GAs provide a robust search method in complex spaces. For this study, a GA is developed to design optimal wind turbines. The GA 22 / Vol. 118, MARCH 1996 Transactions of the ASME Copyright © 1996 by ASME Downloaded From: http://energyresources.asmedigitalcollection.asme.org/ on 06/24/2015 Terms of Use: http://asme.org/terms Downloaded From: http://energyresources.asmedigitalcollection.asme.org/ on 06/24/2015 Terms of Use: http://asme.org/terms

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Section: Applicationmentioning

confidence: 99%

Application of a Genetic Algorithm to Wind Turbine Design

Selig¹,

Coverstone-Carroll

1996

Journal of Energy Resources Technology

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“…The purpose of this study was to generate a data base, similar to an earlier one developed for SHAC simulation models, to guide future analysis and design method development. The results of this analysis were documented in a• separate report (19], and a popularized brochure was developed for the user community [20]. This brochure is quite similar to the SHAC Analysis Method brochure that was first published in 1978, and more than 1,500 copies have been distributed.…”

Section: Wind Energy Systems Studifsmentioning

confidence: 99%

Systems Analysis and Testing (SAT) Program (Annual Progress Report, October 1, 1978 - September 30, 1979)

Murphy¹

1979

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This report was prepared as an account of work sponsored by the United States Government. Neither the United States nor the United States Department of Energy, nor any of their employees, nor any of their contractors, subcontractors, or their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness or usefulness of any information, apparatus, product or process disclosed, or re~resents that its use would not infringe privately owned rights.

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Summary of Currently Used Wind Turbine Performance Prediction Computer Codes

Cited by 2 publications

References 0 publications

Application of a Genetic Algorithm to Wind Turbine Design

Application of a Genetic Algorithm to Wind Turbine Design

Systems Analysis and Testing (SAT) Program (Annual Progress Report, October 1, 1978 - September 30, 1979)

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