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Wind farm layout and hub height optimization with a novel wake model

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  • Sun, Haiying
  • Yang, Hongxing

Abstract

This paper comprehensively investigates the impact of wind turbine layout and hub height on power generation of wind farm. Firstly, an engineering three-dimensional (3-D) wind turbine wake model is improved by the artificial neural network (ANN) technology. The novel 3-D ANN wake model reaches more than 99% accuracy of the original wake model. It can save about 80% of the computational time when predicting the downstream wind speed. Secondly, the influence of wind turbine hub height and position on the equivalent wind speed (EWS) and power is deeply studied. Specially, when reducing the hub height of the downstream wind turbine, both the wake impact from upstream turbines and EWS will decrease, so the overall influence should be assessed according to the specific situation. Finally, the problem of wind farm layout and height optimization is investigated. According to this study, simultaneously optimizing these two factors can obtain a better result than optimizing each factor individually. If economic factor is additionally considered, the optimized hub height and power output results will be quite different. Therefore, considering more factors is important to obtain an appropriate wind farm layout.

Suggested Citation

  • Sun, Haiying & Yang, Hongxing, 2023. "Wind farm layout and hub height optimization with a novel wake model," Applied Energy, Elsevier, vol. 348(C).
    • Handle: RePEc:eee:appene:v:348:y:2023:i:c:s0306261923009182
    DOI: 10.1016/j.apenergy.2023.121554
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    References listed on IDEAS

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    1. Yu, Xiaobing & Lu, Yangchen, 2023. "Reinforcement learning-based multi-objective differential evolution for wind farm layout optimization," Energy, Elsevier, vol. 284(C).
    2. Zhang, Yubao & Chen, Xin & Gong, Sumei & Chen, Jiehao, 2023. "Collective large-scale wind farm multivariate power output control based on hierarchical communication multi-agent proximal policy optimization," Renewable Energy, Elsevier, vol. 219(P2).

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