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Wind Mill Pattern Optimization using Evolutionary Algorithms

Abstract : When designing a wind farm layout, we can reduce the number of variables by optimizing a pattern instead of considering the position of each turbine. In this paper we show that when reducing the problem to only two variables defining a grid, we can gain up to 3% of energy output on simple examples of wind farms dealing with many turbines (up to 1000) while dramatically reducing computation time. To achieve these results, we compared both a genetic algorithm and a differential evolution algorithm to previous results from the literature. These preliminary results should be extended to examples involving non-rectangular farm layouts and wind distributions that may require pattern deformation variables in order to increase solution diversity.
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https://hal-enac.archives-ouvertes.fr/hal-02917483
Contributor : Laurence Porte <>
Submitted on : Wednesday, August 19, 2020 - 12:29:20 PM
Last modification on : Tuesday, August 25, 2020 - 3:29:59 AM

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  • HAL Id : hal-02917483, version 1

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Charlie Vanaret, Nicolas Durand, Jean-Marc Alliot. Wind Mill Pattern Optimization using Evolutionary Algorithms. 2020. ⟨hal-02917483⟩

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