| Summary: | The efficient selection of a wind turbine is presently limited by a developer’s knowledge of what products are available on the market, and their ability to test and compare available turbine designs before investing. Poor turbine selection results in a financially sub-optimal investment. This study applies Blade Element Momentum theory, cost-scaling models and Genetic Algorithms to produce a model that predicts the ideal turbine design for a given site. The model was verified and tested using raw, real-world data from met masts and two Enercon E-44 turbines installed at Búrfell, Iceland. The model identified an optimum wind turbine design for Búrfell which decreases the Levelized Cost of Energy by 10.4% when compared to the existing E-44 turbines. The power curve of the optimum turbine design was then used as a search parameter in a set of real turbines, to determine that the optimum turbine model for Búrfell is the Leitwind LTW70 2MW turbine. The use of this turbine would decrease the Levelized Cost of Energy by 8% when compared to the existing Enercon E-44 turbines. Future recommendations are to develop a similar model using Finite Element Analysis in lieu of Blade Element Momentum theory, and to include optimization of the rotor shape and material. A more up-to-date analysis of wind turbine costs is also advised.
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