Inflow wind field measurements from nacelle-based lidar systems offer great potential for different applications including turbine control, load validation, and power performance measurements. On floating wind turbines nacelle-based lidar measurements are affected by the dynamic behavior of the floating foundations. Therefore, the effects on lidar wind speed measurements induced by floater dynamics must be understood. In this work, we investigate the influence of floater motions on wind speed measurements from forward-looking nacelle-based lidar systems mounted on floating offshore wind turbines (FOWTs) and suggest approaches for correcting motion-induced effects. We use an analytical model, employing the guide for the expression of uncertainty in measurements (GUM) methodology and a numerical lidar simulation for the quantification of uncertainties. It is found that the uncertainty of lidar wind speed estimates is mainly caused by the fore–aft motion of the lidar, resulting from the pitch displacement of the floater. Therefore, the uncertainty is heavily dependent on the amplitude and the frequency of the pitch motion. The bias of 10 min mean wind speed estimates is mainly influenced by the mean pitch angle of the floater and the pitch amplitude. We correct motion-induced biases in time-averaged lidar wind speed measurements with a model-based approach, employing the developed analytical model for uncertainty and bias quantification. Testing of the approach with simulated dynamics from two different FOWT concepts shows good results with remaining mean errors below 0.1 m s⁻¹. For the correction of motion-induced fluctuation in instantaneous measurements, we use a frequency filter to correct fluctuations caused by floater pitch motions for instantaneous measurements. The correction approach's performance depends on the pitch period and amplitude of the FOWT design.

Floating offshore wind turbines (FOWTs) are equipped with various sensors that provide valuable data for turbine monitoring and control. Due to technical and operational challenges, load estimations for mooring lines and fairleads can be difficult and expensive to obtain accurately. This research delves into a methodology where simulated floater motion measurements and wind speed measurements, derived from forward-looking nacelle-based lidar, are utilized as inputs for different types of neural networks to estimate fairlead tension time series and damage equivalent loads (DELs). Fairlead tension is intrinsically linked to the dynamics and the position of the floater. Therefore, we systematically analyze the individual contribution of floater dynamics to the prediction quality of fairlead tension time series and DELs. Wind speed measurements obtained via nacelle-based lidar on floating offshore wind turbines are inherently influenced by the platform's dynamics, notably the rotational pitch displacement and surge displacement of the floater. Consequently, the lidar wind speed data indirectly contain the dynamic behavior of the floater, which, in turn, governs the fairlead loads. This study leverages lidar-measured line-of-sight (LOS) wind speeds to estimate fairlead tensions. Training data for the model are generated by the aeroelastic wind turbine simulation tool, openFAST, in conjunction with the numerical lidar simulation framework ViConDAR. The fairlead tension time series are predicted using long short-term memory (LSTM) networks. DEL predictions are made using three different approaches. First, DELs are calculated from predicted time series; second, DELs are predicted using a sequence-to-one LSTM architecture, and third, DELs are predicted using a convolutional neural network architecture. Results indicate that fairlead tension time series and DELs can be accurately estimated from floater motion time series. Further, we found that lidar LOS measurements do not improve time series or DEL predictions if motion measurements are available. However, using lidar measurements as model inputs for DEL predictions leads to similar accuracies as using displacement measurements of the floater.

This study examines the impact of reliability parameters of critical turbine components on offshore wind farm operation and maintenance (O&M) costs and related greenhouse gas (GhG) emissions. Employing a simulation-based sensitivity analysis, we assess how variations in reliability parameters influence key O&M metrics, including total costs, component-specific breakdowns, availability, intervention frequency, and vessel usage. The associated climate impact is quantified based on vessel fuel consumption and spare part usage. The analysis uses data from the offshore wind farm Lillgrund, incorporating extensive SCADA data and real environmental conditions. For a variation of the reliability parameters by ±50%, results indicate a total variation of up to ±15% in operational expenditures in absolute terms and per MWh of produced electricity. The impact of failure rates on CO ₂ -equivalent emissions is observed to be around ±50% relative to the baseline case for individual components, highlighting the significant influence of reliability on both costs and emissions. These findings underscore the importance of incorporating O&M processes in assessing the GhG footprint of wind energy and establishing a link between turbine reliability and emissions. Additionally, research gaps are identified in understanding the relationship between load conditions and component failure behavior, particularly in the context of wind farm control strategies.

Wind farm operation increasingly requires integrated assessment methods that account for both economic performance and structural lifetime. This paper presents WINPACT, a modular impact-assessment framework that links wake-aware wind-farm response modeling and surrogate-based turbine load prediction with fatigue accumulation, lifetime extension, and techno-economic valuation. The framework is demonstrated using a case study of the Hollandse Kust Noord wind farm, in which a revenue-triggered shutdown strategy curtails production when projected 10-minute farm revenue falls below a defined threshold. Technical impacts are quantified in terms of start-stop frequency, idling time, fatigue damage, and potential lifetime extension under alternative assumptions about non-normal operating loads, and these results are propagated through the economic toolchain to evaluate net present value and levelized cost of electricity under different revenue schemes. Under the investigated assumptions, the strategy leads to increases in net present value and reductions in the levelized cost of electricity, demonstrating the potential of revenue-driven farm control to enhance the long-term economic performance of offshore wind projects.

Inflow wind field measurements from nacelle based lidar systems offer great potential for different applications including turbine control, load validation and power performance measurements. On floating wind turbines nacelle based lidar measurements are affected by the dynamic behaviour of the floating foundations. Therefore, effects on lidar wind speed measurements induced by floater dynamics must be well understood. In this work we investigate the influence of floater motions on wind speed measurements from forward looking nacelle based lidar systems mounted on floating offshore wind turbines (FOWT) and suggest approaches for the correction of motion induced effects. We use an analytical model, employing the GUM methodology and a numerical lidar simulation for the quantification of uncertainties. It is found that the uncertainty of lidar wind speed estimates is mainly caused by fore-aft motion of the lidar, resulting from the pitch displacement of the floater. Therefore, the uncertainty is heavily dependent on the amplitude and the frequency of the pitch motion. The bias of 10 min mean wind speed estimates is mainly influenced by the mean pitch angle of the floater and the pitch amplitude. Further, we discuss the need for motion compensation for different applications of lidar inflow measurements on FOWT and introduce two approaches for the correction of motion induced effects in lidar wind speed measurements. We correct motion induced biases in time averaged lidar wind speed measurements with a model based approach employing the developed analytical model for uncertainty and bias quantification. Testing of the approach with simulated dynamics from two different FOWT concepts shows good results with remaining mean errors below 0.1 ms⁻¹. For the correction of motion induced fluctuation in instantaneous measurements we use a frequency filter to correct fluctuations caused by floater pitch motions for instantaneous measurements. The performance of the correction approach is dependent on the pitch period and amplitude of the FOWT design.

Floating offshore wind turbines are equipped with a diverse array of sensors, offering valuable data for turbine control and monitoring. However, reliable measurements can be elusive and costly for specific physical parameters, particularly load estimations for mooring lines and fairleads. This research delves into a methodology where floater motion measurements and wind speed measurements, derived from forward-looking nacelle-based lidar, are utilized as inputs for different types of neural networks to estimate fairlead tension time series and damage equivalent loads (DELs). Mooring line loads are intrinsically linked to the dynamics and the position of the floater. Therefore, we systematically analyze the individual contribution of floater dynamics on the prediction quality of fairlead tension time series and DELs. Wind speed measurements obtained via nacelle-based lidar on floating offshore wind turbines are influenced inherently by the platform's dynamics, notably the rotational pitch displacement and surge displacement of the floater. Consequently, the lidar wind speed data indirectly contains the dynamic behavior of the floater, which, in turn, governs the fairlead loads. This study directly leverages lidar-measured Line of Sight (LOS) wind speeds to estimate mooring line tensions. Training data for the model is generated by the aero-elastic wind turbine simulation tool, openFAST, in conjunction with the numerical lidar simulation framework ViConDAR. The fairlead tension time series are predicted using long-short-term-memory (LSTM) networks. DEL predictions are made using three different approaches. First, DELs are calculated from predicted time series; second, DELs are predicted using a sequence-to-one LSTM architecture, and third, DELs are predicted using a convolutional neural network architecture. Results indicate that fairlead tension time series and DELs can be accurately estimated from floater motion time series. Further, we found that lidar LOS measurements do not improve time series or DEL predictions if motion measurements are available. However, using lidar measurements as model inputs for DEL predictions leads to similar accuracies as using displacement measurements of the floater.