the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Impact of inflow conditions and turbine placement on the performance of offshore wind turbines exceeding 7 MW
Abstract. Accurately assessing wind turbine performance in large offshore wind farms requires a nuanced understanding of how inflow parameters—turbulence intensity (TI), wind shear, and wind veer—affect power production across different turbine rows. In this study, we analyze 13 months of 10-minute operational data from more than 40 high-capacity turbines in a North Sea offshore wind farm, complemented by nacelle-based LiDAR measurements of inflow. Our objectives are to (1) determine how power production differs between front, middle and rear sections of the farm under the influence of TI, shear, and veer, and (2) evaluate the effectiveness of International Electrotechnical Commission (IEC)–based normalization methods, including Rotor Equivalent Wind Speed (REWS) and turbulence corrections in the front row and inside a wind farm consisting of large-scale wind turbines.
The results indicate that the impact of wind shear and veer on power output is strongly dependent on the turbine location: free stream shear and veer correlate negatively with active power in the front row, yet show positive correlations in the mid and rear rows. In addition, the TI in the wake region has a distinct influence on power production—particularly at lower wind speeds—relative to the TI observed in the free-flow region. Finally, the rear section of the wind farm exhibits approximately 20 % lower variability in active power relative to the front section. These location-specific changes underscore the evolving nature of inflow conditions within large wind farms. Furthermore, IEC-based REWS do not fully capture the effects of shear and veer in a large scale wind turbines in an offshore environment. The findings highlight that turbines operating in non-free-flow conditions may require additional inflow-characterization parameters beyond standard IEC norms to achieve more accurate performance evaluations and enhance overall farm efficiency.
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Status: final response (author comments only)
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RC1: 'Comment on wes-2025-32', Anonymous Referee #1, 25 Mar 2025
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RC2: 'Comment on wes-2025-32', Anonymous Referee #2, 18 May 2025
The manuscript entitled « Impact of inflow conditions and turbine placement on the performance of offshore wind turbines exceeding 7 MW” deals with the correlation between some parameters of the inflow conditions (veer, shear, turbulence intensity) and the power production at some sections of a wind farm. Some corrections as proposed by the IEC standards are applied and their impact on the results are discussed.
Unfortunately, (1) the poor description of the operational database, (2) the absence of proof of validity of the representativeness (as reference inflow measurement) of the nacelle lidar data located at a big distance from the wind farm of interest and, (3) the very low level of correlations (Pearson coefficient mainly lower than 0.3) between the inflow parameters and the power performance make the present manuscript not suitable for publication.
Major comments:
- LiDAR data measurement : (1) No detail on the measurement. Since the lidar is nacelle-mounted, one can assume that the measurement location is following the wind turbine orientation and so, is not always located at the same position. At which distance from the wind turbine is the measurement location? Is it out of the wind farm induction zone? How can you prove this?. (2) The inflow parameters are measured at 8km from the wind farm of interest. How do you prove that these measurements are representative of the inflow impacting the wind farm of interest? Considering the density of wind farms, there are probably strong blockage effects
- Turbine operational regime: were the 10-min periods used when not all wind turbines were under operation during the 10 minutes? If yes, it means that the level of interactions might be modified by the number of running wind turbines in the wind farm
- Wind sector selection: it is 105° wide. The wake interactions are very different depending on the wind direction. This wind sector range is too wide.
- Figure 3 is not cited in the manuscript. It is not clear how the rejected data had been determined.
- 4 to 9: the Pearson Correlation coefficients values are mainly between 0.3 and -0.3. This magnitude is generally considered as a proof of no correlation between two data sets. One would consider that the interpretation of the evolution of the Pearson coefficient with the wind speed, or the comparison between wind farm segments is fully invalid. If it is not the case, the authors need to explain why such low levels of correlations can be interpreted.
Minor comments:
- 2 and Line 91: 50% or 64% of the data are gathered in this wind sector range?
- Lines 87-88 : More details on the DB-scan algorithm
- Fig 3 : “blue” instead of “green”
- 3.3.2 Turbulence intensity correction and Equation 3 : it is not clear what the “simulated average power” refers to. Which simulation?
- Lines 167-168 : explain the Sliding Window Pearson Correlation approach
- 7 is not cited in the manuscript
Citation: https://doi.org/10.5194/wes-2025-32-RC2
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RC2: 'Comment on wes-2025-32', Anonymous Referee #2, 18 May 2025
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