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Developing Realistic Designs for Wind Farms: Incorporation of an Imperialist Competitive Algorithm
1 , 2 , * 3 , 3 , 4
1  Electr. & Comput. Eng. Dept., Ryerson Polytech. Inst., Toronto, Ont., Canada
2  Faculty of Engineering and Applied Science, University of Ontario Institute of Technology, Oshawa, Ontario L1H 7K4, Canada
3  Young Researchers and Elite Club, Islamic Azad University-South Tehran Branch, Tehran, Iran
4  Department of Mechanical and Aerospace Engineering, Syracuse University, 263 Link Hall, Syracuse, NY 13244, USA.

Published: 03 November 2014 by MDPI in The 4th World Sustainability Forum session Energy Sustainability
Abstract: The optimal positioning of wind turbines plays an important role in acquiring the anticipated output power from wind farms. This paper addresses challenges related to typical restriction assumptions for turbine arrangements in wind farms with a candidate selection approach. A hybrid quadratic assignment problem-imperialist competitive algorithm (QAP-ICA) method with an initial candidate points' selection (ICPS) approach is applied to two case studies. This hybrid algorithm is used to obtain optimal layout designs in terms of maximum efficiency. The current study incorporates previously utilized indicators from the literature for wind farms, such as wake effects, turbine hub height, rotor diameter, and transmission losses, and proposes additional criteria such as load-bearing capacity of soil and its restrictions. This is done to make the method applicable for realistic cases, and to assimilate the comments of expert designers. The consequence of an optimal layout design can be superior performance with the proposed algorithm compared to previous similar studies. An efficiency improvement of about 4% is attained for the first case considered, and the algorithm provides reasonable optimal wind farm design layouts for the second case, in which reductions of power losses of the wind farm are considered.
Keywords: Wind farm, wind farm layouts, QAP, ICA, Placement, wind turbines, Iran