The IEC standards prescribe an inflow wind field based on models with empirical assumptions to perform load calculations. The use of preview wind speed measurements using nacelle-mounted Lidars allows mitigation of structural loads by suggesting appropriate control action. This relationship is affected by uncertainty in site conditions and the dynamic control of wind turbine in different operation regimes. Although efforts have been put to derive the relation between the aerodynamic loading and the wind speed, there is scope to explore this relation using measurements. Deriving the load statistics based on the inflow wind field is therefore necessary to establish the correct control strategies. This study focuses on two aspects: firstly, the effect of variability in the inflow conditions due to wind speed fluctuations, turbulence and wind evolution on loading statistics and secondly, the effect of different wind turbine operation modes and Lidar measurement ranges on loading statistics. By understanding the effect of these two aspects on aerodynamic loading, a suitable control strategy can be designed by establishing correlation and coherence based look-up tables with inflow-loading relationship for each operation regime. The study defines a correlation between the Lidar measured wind speed and aerodynamic loading for three Lidar measurement ranges at below and above rated operation modes. Strong correlations are observed at above-rated operation, while uneven correlations at below-rated operation are observed. Strong correlations are observed for turbulence intensities higher than 12%. The preview distances of 50 m and 110 m provide the high correlation estimates The study of coherence between these two signals provides useful insights on the evolution of wind, the length scales present and the frequencies. The coherence remains high for wavelengths upto 200m for above-rated and below-rated operations, the time scale was found to be around 2-2.5 s and the decay parameter ranges from 2 to 7.@en

Wind turbines cannot detect wind speed approaching them, for e.g. gusts. This poses a major risk for fatigue and extreme load failure in the wind turbines. The existing methods using Lidars to estimate wind speeds at the rotor extrapolate the wind speed measured by the Lidar at the last measurement point, (for e.g. 50m). In theory and practise, the blockage effects of the wind turbine, the site conditions, and the atmospheric conditions restrict the validity of Taylor’s frozen turbulence, which is conveniently assumed to be true. Thus, the existing methods estimating the rotor effective wind speed are not reliable and need better models to use the Lidar measurements effectively. This article proposes a real-time wind model to estimate the wind speeds accurately at the rotor by understanding the evolution of wind using Lidars. We use Autoregressive Moving Average Exogenous, ARMAX, models to follow a wind-field or a gust from 200m away to 50m in front of the wind turbine to understand its evolution and dissolution. The ARMAX model for modelling the evolution of a wind field is given by Eqn.(1); where the output wind speed prediction,y(t), is represented by the coefficient A, while the deterministic part of the wind speed u(t - nk) based on past measurements is represented by B, and the stochastic noise part in the wind is characterized by the coefficient C. This understanding can be used to develop a model to incorporate the effects of site and atmospheric conditions on the evolution of wind. It is the intention to also include the blockage effects due to the wind turbine into the model to extrapolate the wind speed estimation through the blind zone of the Lidar to the wind turbine rotor plane.@en

Lidars have become increasingly useful for providing accurate wind speed measurements in front of the wind turbine. The wind field measured at distant meteorological masts changes its structure or was too distorted before it reaches the turbine. Thus, one cannot simply apply Taylor's frozen turbulence for representing this distant flow field at the rotor. Wind turbine controllers can optimize the energy output and reduce the loads significantly, if the wind speed estimates were known in advance with high accuracy and low uncertainty. The current method to derive wind speed estimations from aerodynamic torque, pitch angle and tip speed ratio after the wind field flows past the turbine and have their limitations, e.g. in predicting gusts. Therefore, an estimation model coupled with the measuring capability of nacelle based Lidars was necessary for detecting extreme events and for estimating accurate wind speeds at the rotor disc. Nacelle-mounted Lidars measure the oncoming wind field from utpo 400m(5D) in front of the turbine and appropriate models could be used for deriving the rotor effective wind speed from these measurements. This article proposes an auto-regressive model combined with a method to include the blockage factor in order to estimate the wind speeds accurately using Lidar measurements. An Armax model was used to determine the transfer function that models the physical evolution of wind towards the wind turbine, incorporating the effect of surface roughness, wind shear and wind variability at the site. The model could incorporate local as well as global effects and was able to predict the rotor effective wind speeds with adequate accuracy for wind turbine control actions. A high correlation of 0.86 was achieved as the Armax modelled signal was compared to a reference signal. The model could also be extended to estimate the damage potential during high wind speeds, gusts or abrupt change in wind directions, allowing the controller to act appropriately under extreme conditions.@en

ECN with its partners TU DelŌ, Avent LiDAR Technologies and XEMC Darwind executed the four-year TKI Wind op Zee project LAWINE (LiDAR ApplicaƟon for WInd Energy Efficiency). In this project the applica Ɵon of LiDAR technology has been developed and validated so that it can be used to improve the operaƟon of offshore wind farms with the goal to further reduce the cost of offshore wind power plants. The planned deliverables in the project have been met within Ɵme and budget requirements. Gathering a project team that includes a variety of competences has resulted in a fruiƞul cooperaƟon leading to the following interesƟng project results: 1. It has been verified that ground based LiDAR can be applied for wind resource assessments as well as power and loads assessment campaigns. This way wind turbine performance can be verified without the requirement of expensive masts. 2. Power performance assessments can be performed more accurately by using the wind profile measurements of the LiDAR. 3. Wake characterizaƟon by LiDAR measurements has been demonstrated, which will assist the opƟ- mizaƟon of lay-outs of offshore wind farms. 4. In the project it has been demonstrated that nacelle LiDARs are suitable to be used as basis in power performance assessments. 5. It has been demonstrated that LiDAR measurements in combinaƟon with advanced controllers have significant benefits for rotor speed regulaƟon and reducƟon of faƟgue loads.6. It has been demonstrated that LiDARs can determine yaw misalignment accurately, which is an important aspect to implement the ECN wind farm controllers. 7. ECN developed the LiDAR calibraƟon facility where industry can calibrate its LiDARs in a very effecƟve manner. Such uƟlity is crucial for the successful applicaƟon of LiDARs in wind resource assessments and power performance verificaƟon. These results have been disseminated with researchers and industry over various conferences and workshops. @en

Lidars have gained a lot of popularity in the field of wind energy, partly because of their potential to be used for wind turbine control. By scanning the oncoming wind field, any threats such as gusts can be detected early and high loads can be avoided by taking preventive actions. Unfortunately, lidars suffer from some inherent weaknesses that hinder measuring gusts; e.g., the averaging of high-frequency fluctuations and only measuring along the line of sight). This paper proposes a method to construct a useful signal from a lidar by fitting a homogeneous Gaussian velocity field to a set of scattered measurements. The output signal, an along-wind force, acts as a measure for the damaging potential of an oncoming gust and is shown to agree with the rotor-effective wind speed (a similar control input, but derived directly from the wind turbine’s shaft torque). Low data availability and the disadvantage of not knowing the velocity between the lidar beams is translated into uncertainty and integrated in the output signal. This allows a designer to establish a control strategy based on risk, with the ultimate goal to reduce the extreme loads during operation. @en