Renewable energy, particularly wind power, plays a vital role in combating global warming and air pollution. However, the inherent variability of wind complicates turbine performance predictions, especially for offshore wind farms. Standardised power curve models often fail to account for site-specific conditions, leading to performance deviations.

This thesis addresses these limitations by developing a new method to correct power curve predictions using detailed, site-specific data. Nacelle-mounted LiDAR and SCADA data from RWE’s offshore wind farms are utilised to enhance performance assessments during the development phase. Existing methods only rely on turbulence intensity (TI) to explain performance deviations, but these methods are based on smaller, onshore turbines and do not adequately account for the complexities of larger offshore turbines. The research introduces power performance matrices that incorporate additional wind characteristics such as shear, veer, and yaw misalignment, rather than relying solely on TI. The study finds that while LiDAR is less effective in measuring TI, it excels at capturing shear and veer, both of which show consistent patterns in relation to turbine performance. The findings reveal that extreme shear values negatively affect power production, while neutral or low shear has positive effects. Similarly, high veer correlates with decreased performance. 

Based on these insights, new power performance matrices were developed, using shear, veer, and TI, along with normalised wind speed, to adjust the power curve. These matrices can be dynamically created using site-specific data, making them flexible and adaptable for different turbines and wind conditions. The method enables selecting “inner range” parameters, providing a more accurate reference for power deviations. Additionally, this approach is particularly advantageous because shear and veer are well-captured by both floating and nacelle LiDAR, making it applicable across various stages of wind farm development. The primary limitation of this method is its reduced effectiveness in regions with significant mechanical atmospheric mixing, such as near coastal or mountainous terrain. In such areas, local topography can create complex wind flows, requiring more parameters than shear and veer to fully capture the wind dynamics. Another limitation is wake analysis; despite directional filtering to remove wake effects, some residual wake impact persists, suggesting further refinement is needed. 

Although more data is necessary to fully validate the approach, the current methodology shows promise in enhancing energy yield estimations and improving long-term predictions. The use of site-specific power matrices, particularly for offshore turbines, could significantly increase the accuracy of performance predictions, benefiting wind farm developers and manufacturers. As more data from LiDAR measurements and power performance tests become available, the matrices can be adapted to different turbine types and conditions by normalising wind speeds and creating dynamic “inner ranges” for site-specific use. Future research should focus on refining wake filtering methods and understanding the full correlation between shear, veer, and TI to further enhance prediction accuracy. In conclusion, this thesis proposed a novel method for adjusting turbine power predictions using site-specific wind characteristics, with potential to greatly improve the reliability of performance estimates. By integrating shear and veer into the power curve adjustments, this approach allows more reliable performance estimations. With further data collection, this methodology holds the potential to become a key tool for optimising wind energy production in the future.

In recent years, the use of renewable energy sources has increased significantly. As a result, offshore wind energy has expanded due to its advantages compared to onshore, such as steadier winds, reduced visual impact and lower noise emissions. However, 80% of the global offshore wind resource potential is located in areas with water depths greater than 60 meters, making the installation of fixed-bottom wind turbines unfeasible and leading to an increase in floating wind turbines. Floating offshore wind turbines, due to their increased freedom of movement, experience more complex aerodynamic and hydrodynamic phenomena, resulting in different power generation compared to fixed-bottom wind turbines. The effect of six degrees of freedom on power production has not been extensively studied in full-scale operating wind turbines. Additionally, a better prediction of energy production could also reduce the investment risk of floating wind turbines, leading to a reduced levelised cost of energy and better integration of floating wind turbines into a fully renewable energy system.

The aim of this report was to investigate the power generation and response of the TetraSpar demonstrator project, the world’s first fully industrialised floating offshore foundation. To achieve this, a model of the TetraSpar demonstrator was created in OpenFAST. Due to confidentiality, some data, particularly regarding the wind turbine, were unavailable and were instead based on scaling the NREL 5 MW wind turbine. In addition, the ROSCO controller was incorporated into the model and the floating platform was modelled as a six degrees of freedom rigid body.

For the validation of the OpenFAST model, a comparison was performed with the on-site TetraSpar demonstrator. Data for the on-site demonstrator regarding motions, metocean conditions and power generation were provided by RWE. Further filtering and averaging the data in 10-minute periods for an entire year was performed. First, three one-hour simulations were performed for three different operating conditions: cut-in, below rated and above rated. Results from time series and power spectral density analyses showed a good agreement in platform motions. For the generated power, the mean average produced power was closest to the on-site data in the above rated region.

Next, for the power curve and AEP estimation, the probability of occurrence for each wind speed in the operating region was determined based on the on-site measured data. Furthermore, for every wind speed, the most representative significant wave height, wave period, turbulence intensity and other parameters needed as inputs in OpenFAST were found. The results showed that the AEP predicted by OpenFAST was 2.8% lower than the AEP measured at the on-site TetraSpar demonstrator. Moreover, different peak shaving levels of the controller contributed to the AEP percentage difference, ranging from about 4.4% to 2.1% compared to the on-site measurements. As far as platform motions are concerned, the model showed good agreement, capturing the trend of mean values and standard deviations of the two most dominant motions in power production, surge and pitch. In addition, the effect of wave height and wave period showed that the mean generated power is slightly affected. Similar results were also found for different current velocities. However, waves were found to influence the oscillation amplitudes of surge and pitch motions, while currents mainly affected the shift in mean values. Lastly, wind-wave misalignment also proved to have a negative effect on power performance.

In conclusion, the current OpenFAST model can be used to estimate the energy production and platform motion of the TetraSpar demonstrator. However, further improvements and reduction of the assumptions made, mainly due to the unavailability of data because of confidentiality, could enhance its accuracy.