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Wind Turbine Clustering Algorithm of Large Offshore Wind Farms considering Wake Effects

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  • Binbin Zhang
  • Jun Liu

Abstract

This paper proposed the SVD (singular value decomposition) clustering algorithm to cluster wind turbines into some group for a large offshore wind farm, in order to reduce the high-dimensional problem in wind farm power control and numerical simulation. Firstly, wind farm wake relationship matrixes are established considering the wake effect in an offshore wind farm, and the SVD of wake relationship matrixes is used to cluster wind turbines into some groups by the fuzzy clustering algorithm. At last, the Horns Rev offshore wind farm is analyzed to test the clustering algorithm, and the clustering result and the power simulation show the effectiveness and feasibility of the proposed clustering strategy.

Suggested Citation

  • Binbin Zhang & Jun Liu, 2019. "Wind Turbine Clustering Algorithm of Large Offshore Wind Farms considering Wake Effects," Mathematical Problems in Engineering, Hindawi, vol. 2019, pages 1-7, September.
    • Handle: RePEc:hin:jnlmpe:6874693
    DOI: 10.1155/2019/6874693
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    Cited by:

    1. Michiel Dhont & Elena Tsiporkova & Veselka Boeva, 2021. "Advanced Discretisation and Visualisation Methods for Performance Profiling of Wind Turbines," Energies, MDPI, vol. 14(19), pages 1-30, September.
    2. Khadijah Barashid & Amr Munshi & Ahmad Alhindi, 2023. "Wind Farm Power Prediction Considering Layout and Wake Effect: Case Study of Saudi Arabia," Energies, MDPI, vol. 16(2), pages 1-22, January.
    3. Yanfang Chen & Young-Hoon Joo & Dongran Song, 2021. "Modified Beetle Annealing Search (BAS) Optimization Strategy for Maxing Wind Farm Power through an Adaptive Wake Digraph Clustering Approach," Energies, MDPI, vol. 14(21), pages 1-24, November.

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