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Optimal placement of wind turbines within a wind farm considering multi-directional wind speed using two-stage genetic algorithm

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Abstract

Most wind turbines within wind farms are set up to face a pre-determined wind direction. However, wind directions are intermittent in nature, leading to less electricity production capacity. This paper proposes an algorithm to solve the wind farm layout optimization problem considering multi-angular (MA) wind direction with the aim of maximizing the total power generated on wind farms and minimizing the cost of installation. A twostage genetic algorithm (GA) equipped with complementary sampling and uniform crossover is used to evolve a MA layout that will yield optimal output regardless of the wind direction. In the first stage, the optimal wind turbine layouts for 8 different major wind directions were determined while the second stage allows each of the previously determined layouts to compete and inter-breed so as to evolve an optimal MA wind farm layout. The proposed MA wind farm layout is thereafter compared to other layouts whose turbines have focused site specific wind turbine orientation. The results reveal that the proposed wind farm layout improves wind power production capacity with minimum cost of installation compared to the layouts with site specific wind turbine layouts. This paper will find application at the planning stage of wind farm.

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Acknowledgements

The authors want to thank the University of Ibadan for the conducive environment during the course of the research.

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Authors and Affiliations

  1. Power Energy Machines and Drives (PEMD) Research Group, Department of Electrical and Electronic Engineering, University of Ibadan, Ibadan, Nigeria

    A. S. O. Ogunjuyigbe, T. R. Ayodele & O. D. Bamgboje

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  1. A. S. O. Ogunjuyigbe
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  2. T. R. Ayodele
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  3. O. D. Bamgboje
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Correspondence to T. R. Ayodele.

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Ogunjuyigbe, A.S.O., Ayodele, T.R. & Bamgboje, O.D. Optimal placement of wind turbines within a wind farm considering multi-directional wind speed using two-stage genetic algorithm. Front. Energy 15, 240–255 (2021). https://doi.org/10.1007/s11708-018-0514-x

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  • DOI: https://doi.org/10.1007/s11708-018-0514-x

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