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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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Submitted on : Wednesday, August 19, 2020 - 12:29:20 PM
Last modification on : Wednesday, November 3, 2021 - 4:18:09 AM
Long-term archiving on: : Monday, November 30, 2020 - 9:40:32 PM


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  • HAL Id : hal-02917483, version 1


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



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