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.