Improved Data-Driven Yaw Misalignment Calibration of Wind Turbine via LiDAR Verification

Qu, Chenzhi; Lin, Zongli; Han, Xiangyu; Wang, Chuanxi; Wu, Qing; Li, Xiongwei; Zhang, Zonghui; Gong, Yanfeng; Jiang, Guangwen

doi:10.1109/cac51589.2020.9326891

Cited by 1 publication

(1 citation statement)

References 9 publications

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“…Giyanani et al [32] presented a correlation between LiDAR-based wind speed and aerodynamic loading for three LiDAR measurement ranges below and above the rated operation modes. Qu et al [33] described data-driven yaw misalignment calibrations of a wind turbine via LiDAR verification and proposed a yaw zero-point misalignment calibration method to improve wind direction signal accuracy. Chen et al [34] proposed a two-step Cholesky decomposition for factorizing coherence matrices in wind field generation.…”

Section: Introductionmentioning

confidence: 99%

A Simple Model for Wake-Induced Aerodynamic Interaction of Wind Turbines

Mahmoodi,

Khezri,

Ebrahimi

et al. 2023

Energies

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Wind turbine aerodynamic interactions within wind farms lead to significant energy losses. Optimizing the flow between turbines presents a promising solution to mitigate these losses. While analytical models offer a fundamental approach to understanding aerodynamic interactions, further development and refinement of these models are imperative. We propose a simplified analytical model that combines the Gaussian wake model and the cylindrical vortex induction model to evaluate the interaction between wake and induction zones in 3.5 MW wind turbines with 328 m spacing. The model’s validation is conducted using field data from a nacelle-mounted LiDAR system on the downstream turbine. The ‘Direction to Hub’ parameter facilitates a comparison between the model predictions and LiDAR measurements at distances ranging from 50 m to 300 m along the rotor axis. Overall, the results exhibit reasonable agreement in flow trends, albeit with discrepancies of up to 15° in predicting peak interactions. These deviations are attributed to the single-hat Gaussian shape of the wake model and the absence of wake expansion consideration, which can be revisited to improve model fidelity. The ‘Direction to Hub’ parameter proves valuable for model validation and LiDAR calibration, enabling a detailed flow analysis between turbines. This analytical modeling approach holds promise for enhancing wind farm efficiency by advancing our understanding of turbine interactions.

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Section: Introductionmentioning

confidence: 99%