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Evaluation of alternative power production efficiency metrics for offshore wind turbines and farms

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  • Niu, Briana
  • Hwangbo, Hoon
  • Zeng, Li
  • Ding, Yu

Abstract

The use of power production efficiency metrics for wind turbines is important for evaluating their productivity and quantifying the effectiveness of actions that are meant to improve the energy production. The goal of this research is not to propose a new efficiency metric since there are already multiple efficiency metrics widely used in practice: availability, power generation ratio, and power coefficient. Our objective here is to sort out the question of how these efficiency metrics are related to, or different from, one another. We believe addressing this research question has a great degree of practical significance as it is a question practitioners are often puzzled with. Understanding the similarities and differences of multiple efficiency metrics may even lay a foundation for the future proposals of new efficiency metrics. Our evaluation of whether the existing metrics are consistent with each other is driven by the use of actual data from an offshore wind farm. We observe that the three metrics show some degree of consistency but the power generation ratio, albeit the least popular, appears more representative of all metrics and more illustrative of the underlying efficiency. We also found that there is about 4% efficiency difference between wake-free and in-the-wake turbines for this specific wind farm.

Suggested Citation

  • Niu, Briana & Hwangbo, Hoon & Zeng, Li & Ding, Yu, 2018. "Evaluation of alternative power production efficiency metrics for offshore wind turbines and farms," Renewable Energy, Elsevier, vol. 128(PA), pages 81-90.
    • Handle: RePEc:eee:renene:v:128:y:2018:i:pa:p:81-90
    DOI: 10.1016/j.renene.2018.05.050
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    Cited by:

    1. Yanhui Qiao & Yongqian Liu & Yang Chen & Shuang Han & Luo Wang, 2022. "Power Generation Performance Indicators of Wind Farms Including the Influence of Wind Energy Resource Differences," Energies, MDPI, vol. 15(5), pages 1-25, February.
    2. He, Jiao & Jin, Xin & Xie, S.Y. & Cao, Le & Lin, Yifan & Wang, Ning, 2019. "Multi-body dynamics modeling and TMD optimization based on the improved AFSA for floating wind turbines," Renewable Energy, Elsevier, vol. 141(C), pages 305-321.
    3. Ding, Yu & Kumar, Nitesh & Prakash, Abhinav & Kio, Adaiyibo E. & Liu, Xin & Liu, Lei & Li, Qingchang, 2021. "A case study of space-time performance comparison of wind turbines on a wind farm," Renewable Energy, Elsevier, vol. 171(C), pages 735-746.
    4. Nasery, Praanjal & Aziz Ezzat, Ahmed, 2023. "Yaw-adjusted wind power curve modeling: A local regression approach," Renewable Energy, Elsevier, vol. 202(C), pages 1368-1376.
    5. Golparvar, Behzad & Papadopoulos, Petros & Ezzat, Ahmed Aziz & Wang, Ruo-Qian, 2021. "A surrogate-model-based approach for estimating the first and second-order moments of offshore wind power," Applied Energy, Elsevier, vol. 299(C).
    6. Oscar Carranza Castillo & Viviana Reyes Andrade & Jaime José Rodríguez Rivas & Rubén Ortega González, 2023. "Comparison of Power Coefficients in Wind Turbines Considering the Tip Speed Ratio and Blade Pitch Angle," Energies, MDPI, vol. 16(6), pages 1-23, March.

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