Abstract
This work proposes a new wave-period estimation (L-dB) method based on the power-spectral-density (PSD) estimation of pitch and roll motional time series of a Doppler wind lidar buoy under the assumption of small angles (±22 deg) and slow yaw drifts (1 min), and the neglection of translational motion. We revisit the buoy’s simplified two-degrees-of-freedom (2-DoF) motional model and formulate the PSD associated with the eigenaxis tilt of the lidar buoy, which was modelled as a complex-number random process. From this, we present the L-dB method, which estimates the wave period as the average wavelength associated to the cutoff frequency span at which the spectral components drop off L decibels from the peak level. In the framework of the IJmuiden campaign (North Sea, 29 March–17 June 2015), the L-dB method is compared in reference to most common oceanographic wave-period estimation methods by using a TriaxysTM buoy. Parametric analysis showed good agreement (correlation coefficient, = 0.86, root-mean-square error (RMSE) = 0.46 s, and mean difference, MD = 0.02 s) between the proposed L-dB method and the oceanographic zero-crossing method when the threshold L was set at 8 dB.
Highlights
In the last few decades, there has been rising interest in offshore wind energy due to higher and more homogeneous winds that can be found in open sea environments [1].High investments in offshore-wind-farm deployment and operation have been made in Europe in recent years [2]
In order to validate the proposed methodology, TL−dB estimations (Equation (24)) were carried out over tilt experimental data measured during the whole IJmuiden campaign (80 days) and compared against reference wave periods measured by the TriaxysTM buoy
When comparing the significant wave period estimated via the L-dB method and TriaxysTM, TL−dB was resampled to the temporal resolution of TriaxysTM (1 h)
Summary
In the last few decades, there has been rising interest in offshore wind energy due to higher and more homogeneous winds that can be found in open sea environments [1]. High investments in offshore-wind-farm deployment and operation have been made in Europe in recent years [2]. Offshore wind energy (WE) is still one of the most expensive energy sources [3] and needs cost optimization in order to achieve commercial competitiveness. One of the main concerns in the WE industry is obtaining trustworthy data to assess the feasibility of future offshore-wind-farm locations. Meteorological masts (metmasts) have been traditionally used for this purpose. Their high cost has produced the need for alternative atmosphere-assessment methods
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