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A novel analytical wake model for mountain wind farms considering variable surface roughness and wake effects of near-middle region

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  • Wang, Bingchen
  • Ding, Lifu
  • Xiao, Tannan
  • Chen, Ying
  • Lu, Qiuyu

Abstract

In comparison to offshore wind farms and those situated on flat terrains, mountain wind farms exhibit distinct characteristics, such as reduced distances between wind turbines and a higher degree of surface roughness in the surrounding environment. These inherent characteristics present significant challenges for the application of existing analytical models. For instance, the capability of the wake velocity model to accurately predict conditions in the near-middle wake regions has been put under scrutiny. Simultaneously, the wake turbulence intensity model is required to adjust to the distribution of turbulence intensity under conditions of heightened surface roughness. Moreover, the existing parameter calibration algorithms for wake model prove challenging to implement for an observed operational wind farm. To address these issues for mountain wind farms, an analytical wake model with online parameter calibration is proposed. The new wake velocity model significantly improves upon the limitations of the Shen model, particularly in the critical near-middle wake regions. Meanwhile, the wake turbulence model introduces a sophisticated prediction of vertical distribution under various surface roughness conditions. The model’s enhancements are rigorously validated through extensive experimental data and Supervisory Control And Data Acquisition (SCADA) data, confirming its superior predictive capabilities and the effectiveness of the online calibration algorithm.

Suggested Citation

  • Wang, Bingchen & Ding, Lifu & Xiao, Tannan & Chen, Ying & Lu, Qiuyu, 2025. "A novel analytical wake model for mountain wind farms considering variable surface roughness and wake effects of near-middle region," Renewable Energy, Elsevier, vol. 243(C).
    • Handle: RePEc:eee:renene:v:243:y:2025:i:c:s0960148125001041
    DOI: 10.1016/j.renene.2025.122442
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    References listed on IDEAS

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