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Degree

Master of Sciences (Engineering), Case Western Reserve University, EMC - Fluid and Thermal Engineering, 2010.

Abstract

In this study, a code ‘Wind Farm Optimization using a Genetic Algorithm’ (referred as WFOG) is developed in MATLAB for optimizing the placement of wind turbines in large wind farms to minimize the cost per unit power produced from the wind farm. A genetic algorithm is employed for the optimization. WFOG is validated using the results from previous studies. The grid spacing (distance between two nodes where a wind turbine can be placed) is reduced to 1/40 wind turbine rotor diameter as compared to 5 rotor diameter in previous studies. Results are obtained for three different wind regimes: Constant wind speed and fixed wind direction, constant wind speed and variable wind direction, and variable wind speed and variable wind direction. Cost per unit power is reduced by 11.7 % for Case 1, 11.8 % for Case 2, and 15.9 % for Case 3 for results obtained using WFOG. The advantages/benefits of a refined grid spacing of 1/40 rotor diameter (1 m) are evident and are discussed.

Subject Headings

Energy; Mechanical engineering

Keywords

Wind turbine; micro-siting; optimization; wind turbine wake; genetic algorithm

Committee / Advisors

J. Iwan D. Alexander, PhD (Committee Chair)
Alexis R. Abramson, PhD (Committee Member)
Jaikrishnan R. Kadambi, PhD (Committee Member)
Joseph M. Prahl, PhD (Committee Member)

Pages

90p.

Document number: case1270056861
Permalink: http://rave.ohiolink.edu/etdc/view?acc_num=case1270056861

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