A data-driven approach for identification and compensation of wind turbine inherent yaw misalignment

Bao, Yunong; Yang, Qinmin; Li, Siliang; Miao, Kuangwei; Sun, Youxian

doi:10.1109/yac.2018.8406510

Cited by 4 publications

(1 citation statement)

References 17 publications

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“…As regards the approach based on power curve analysis, the main studies are the following: In [5], a quite straightforward analysis of the binned power curve is employed, upon data pre-processing which consists of grouping per yaw error intervals of 2 • . A similar approach is employed in [15,16]: the data are grouped per yaw error intervals and the power curve is analyzed through a Least-Square B-spline Approximation. In the above studies, the systematic yaw error is identified as the measured yaw error at which the best average performance is achieved.…”

Section: Literature Reviewmentioning

confidence: 99%

Individuation of Wind Turbine Systematic Yaw Error through SCADA Data

et al. 2022

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Section: Literature Reviewmentioning

confidence: 99%

Individuation of Wind Turbine Systematic Yaw Error through SCADA Data

et al. 2022

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Multi‐dimensional evaluation and diagnostic methods for wind turbine power generation performance based on different influencing factors

Chen,

Wang,

Xie

et al. 2024

IET Renewable Power Gen

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The power generation performance of wind turbines has consistently been a paramount concern for wind power operators, maintainers, and manufacturers, as it directly determines the profitability of wind farms. However, due to the combined influence of complex environmental conditions within wind farms and inherent deficiencies in wind turbine design, significant variations in power generation performance persist among turbines of the same model. This discrepancy can be attributed to two crucial factors: site conditions and operational efficiency. To achieve more precise and systematic diagnostic work on the power generation performance of wind turbines, this paper focuses on three factors: air density, turbulence intensity, and yaw adaptability. Based on this, three evaluation and diagnosis methods are proposed, including a conversion method for air density based on two‐dimensional interpolation, a turbulence correction method based on the zero‐turbulence curve, and a yaw adaptability diagnosis method based on the convergence degree. Finally, the effectiveness of these proposed methods is verified through the analysis of actual wind field data.

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