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Power Maximization and Turbulence Intensity Management through Axial Induction-Based Optimization and Efficient Static Turbine Deployment

Author

Listed:
  • Mfon Charles

    (Department of Electrical Engineering, University of Cape Town, Rondebosch, Cape Town 7700, South Africa)

  • David T. O. Oyedokun

    (Department of Electrical Engineering, University of Cape Town, Rondebosch, Cape Town 7700, South Africa)

  • Mqhele Dlodlo

    (Department of Electrical Engineering, University of Cape Town, Rondebosch, Cape Town 7700, South Africa
    Vice-Chancellor’s Department, National University of Science and Technology (NUST), Corner, Gwanda Road and Cecil Avenue, Bulawayo P.O. Box AC939, Zimbabwe)

Abstract

Layout optimization is capable of increasing turbine density and reducing wake effects in wind plants. However, such optimized layouts do not guarantee fixed T-2-T distances in any direction and would be disadvantageous if reduction in computational costs due to turbine set-point updates is also a priority. Regular turbine layouts are considered basic because turbine coordinates can be determined intuitively without the application of any optimization algorithms. However, such layouts can be used to intentionally create directions of large T-2-T distances, hence, achieve the gains of standard/non-optimized operations in these directions, while also having close T-2-T distances in other directions from which the gains of optimized operations can be enjoyed. In this study, a regular hexagonal turbine layout is used to deploy turbines within a fixed area dimension, and a turbulence intensity-constrained axial induction-based plant-wide optimization is carried out using particle swarm, artificial bee colony, and differential evolution optimization techniques. Optimized plant power for three close turbine deployments (4 D , 5 D , and 6 D ) are compared to a non-optimized 7 D deployment using three mean wind inflows. Results suggest that a plant power increase of up to 37% is possible with a 4 D deployment, with this increment decreasing as deployment distance increases and as mean wind inflow increases.

Suggested Citation

  • Mfon Charles & David T. O. Oyedokun & Mqhele Dlodlo, 2021. "Power Maximization and Turbulence Intensity Management through Axial Induction-Based Optimization and Efficient Static Turbine Deployment," Energies, MDPI, vol. 14(16), pages 1-23, August.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:16:p:4943-:d:613333
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    References listed on IDEAS

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    1. Amin Niayifar & Fernando Porté-Agel, 2016. "Analytical Modeling of Wind Farms: A New Approach for Power Prediction," Energies, MDPI, vol. 9(9), pages 1-13, September.
    2. Chowdhury, Souma & Zhang, Jie & Messac, Achille & Castillo, Luciano, 2013. "Optimizing the arrangement and the selection of turbines for wind farms subject to varying wind conditions," Renewable Energy, Elsevier, vol. 52(C), pages 273-282.
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