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IEA Wind Task 32 serves as an international platform for the research community and industry to identify and mitigate barriers to the use of lidars in wind energy applications. The workshop “Optimizing Lidar Design for Wind Energy Applications” was held in July 2016 to identify lidar system properties that are desirable for wind turbine control applications and help foster the widespread application of lidar-assisted control (LAC). One of the main barriers this workshop aimed to address is the multidisciplinary nature of LAC. Since lidar suppliers, wind turbine manufacturers, and researchers typically focus on their own areas of expertise, it is possible that current lidar systems are not optimal for control purposes. This paper summarizes the results of the workshop, addressing both practical and theoretical aspects, beginning with a review of the literature on lidar optimization for control applications. Next, barriers to the use of lidar for wind turbine control are identified, such as availability and reliability concerns, followed by practical suggestions for mitigating those barriers. From a theoretical perspective, the optimization of lidar scan patterns by minimizing the error between the measurements and the rotor effective wind speed of interest is discussed. Frequency domain methods for directly calculating measurement error using a stochastic wind field model are reviewed and applied to the optimization of several continuous wave and pulsed Doppler lidar scan patterns based on commercially-available systems. An overview of the design process for a lidar-assisted pitch controller for rotor speed regulation highlights design choices that can impact the usefulness of lidar measurements beyond scan pattern optimization. Finally, using measurements from an optimized scan pattern, it is shown that the rotor speed regulation achieved after optimizing the lidar-assisted control scenario via time domain simulations matches the performance predicted by the theoretical frequency domain model.

The recent developments in renewable energy have led to a higher proportion of converter-connected power generation sources in the grid. Operating a high renewable energy penetration power system and ensuring the frequency stability could be challenging due to the reduced system inertia, which is usually provided by the conventional synchronous generators. Previous studies have shown the potential of wind turbines to provide an inertia response to the grid based on the measured rate of change of the grid frequency. This is achieved by controlling the kinetic energy extraction from the rotating parts by its converters. In this paper, we derive a spectral-based model of the grid frequency by analyzing historical measurements. The spectral model is then used to generate realistic, generic, and stochastic signals of the grid frequency for typical aero-elastic simulations of wind turbines. The spectral model enables the direct assessment of the additional impact of the inertia response control on wind turbines: the spectra of wind turbine output signals such as generator speed, tower base bending moment, and shaft torsional moment are calculated directly from the developed spectral model of the grid frequency and a commonly used spectral model of the turbulent wind. The calculation of output spectra is verified with non-linear time-domain simulations and spectral estimation. Based on this analysis, a notch filter is designed to significantly alleviate the negative impact on wind turbine’s structural loads due to the inertia response with only a small reduction on the grid support.

The wind direction is closely linked to the power performance and structural loads of wind turbines. Conventional nacelle‐mounted vanes or sonic anemometers face errors associated with airflow distortions caused by turbine blades. Nacelle‐mounted lidar systems offer line‐of‐sight speed measurements from multiple positions ahead of the rotor and rely on wind field reconstruction methods to predict the wind direction. This work considers three methods: the matrix inverse, the velocity azimuth display, and the physics‐informed neural network (PINN)–based methods. The first two are industrialized techniques that assume homogeneous flow. For flat terrain and offshore sites, the inhomogeneity of the mean flow is influenced by time‐averaging windows and turbine wakes. To illustrate the limitations and potential bias of wind direction estimates with homogeneous flow assumptions, we first present the bias using site measurement data. We then formulate a theoretical bias for a typical two‐beam lidar system. Next, we use openly available large eddy simulation data to evaluate the minute‐averaged wind direction estimates produced by the three methods. The first two methods are found to be unreliable, with maximum errors reaching close to 25° in the unwaked scenario and exceeding 30° in the waked case. As for the PINN‐based method, the errors remain within 10° across unwaked, waked, nonyawed, and yawed scenarios, even when only a 2D nonlinear convection equation is used as the physical constraint.

The demand for minute-scale forecasts of wind power is continuously increasing with the growing penetration of renewable energy into the power grid, as grid operators need to ensure grid stability in the presence of variable power generation. For this reason, IEA Wind Tasks 32 and 36 together organized a workshop on “Very Short-Term Forecasting of Wind Power” in 2018 to discuss different approaches for the implementation of minute-scale forecasts into the power industry. IEA Wind is an international platform for the research community and industry. Task 32 tries to identify and mitigate barriers to the use of lidars in wind energy applications, while IEA Wind Task 36 focuses on improving the value of wind energy forecasts to the wind energy industry. The workshop identified three applications that need minute-scale forecasts: (1) wind turbine and wind farm control, (2) power grid balancing, (3) energy trading and ancillary services. The forecasting horizons for these applications range from around 1 s for turbine control to 60 min for energy market and grid control applications. The methods that can be applied to generate minute-scale forecasts rely on upstream data from remote sensing devices such as scanning lidars or radars, or are based on point measurements from met masts, turbines or profiling remote sensing devices. Upstream data needs to be propagated with advection models and point measurements can either be used in statistical time series models or assimilated into physical models. All methods have advantages but also shortcomings. The workshop’s main conclusions were that there is a need for further investigations into the minute-scale forecasting methods for different use cases, and a cross-disciplinary exchange of different method experts should be established. Additionally, more efforts should be directed towards enhancing quality and reliability of the input measurement data.

IEA Wind Task 32 exists to identify and mitigate barriers to the adoption of lidar for wind energy applications. It leverages ongoing international research and development activities in academia and industry to investigate site assessment, power performance testing, controls and loads, and complex flows. Since its initiation in 2011, Task 32 has been responsible for several recommended practices and expert reports that have contributed to the adoption of ground-based, nacelle-based, and floating lidar by the wind industry. Future challenges include the development of lidar uncertainty models, best practices for data management, and developing community-based tools for data analysis, planning of lidar measurements and lidar configuration. This paper describes the barriers that Task 32 identified to the deployment of wind lidar in each of these application areas, and the steps that have been taken to confirm or mitigate the barriers. Task 32 will continue to be a meeting point for the international wind lidar community until at least 2020 and welcomes old and new participants.

This work presents the results from a field test of LIDAR assisted collective pitch control using a scanning LIDAR device installed on the nacelle of a mid-scale research turbine. A nonlinear feedforward controller is extended by an adaptive filter to remove all uncorrelated frequencies of the wind speed measurement to avoid unnecessary control action. Positive effects on the rotor speed regulation as well as on tower, blade and shaft loads have been observed in the case that the previous measured correlation and timing between the wind preview and the turbine reaction are accomplish. The feedforward controller had negative impact, when the LIDAR measurement was disturbed by obstacles in front of the turbine. This work proves, that LIDAR is valuable tool for wind turbine control not only in simulations but also under real conditions. Furthermore, the paper shows that further understanding of the relationship between the wind measurement and the turbine reaction is crucial to improve LIDAR assisted control of wind turbines.

Different certification procedures in wind energy, such as power performance testing or load estimation, require measurements of the wind speed, which is set in relation to the electrical power output or the turbine loading. The wind shear affects the behavior of the turbine as hub heights and rotor diameters of modern wind turbines increase. Different measurement methods have been developed to take the wind shear into account. In this paper an approach is presented where the wind speed is measured from the nacelle of a wind turbine using a scanning lidar system. The measurement campaign was performed on the two-bladed Controls Advanced Research Turbine (CART-2) at the National Wind Technology Center in Colorado. The wind speed of the turbine inflow was measured and recalculated in three different ways: using an anemometer installed on a meteorological mast, using the nacelle-based lidar scanner, and using the wind turbine itself. Here, the wind speed was recalculated from turbine data using the wind turbine as a big horizontal anemometer. Despite the small number of useful data, the correlation between this so-called rotor effective wind speed and the wind speed measured by the scanning nacelle-based lidar is high. It could be demonstrated that a nacelle-based scanning lidar system provides accurate measurements of the wind speed converted by a wind turbine. This is a first step, and it provides evidence to support further investigations using a much more extensive dataset and refines the parameters in the measurement process.

Using the inflowing horizontal and vertical wind shears for individual pitch controller is a promising method if blade bending measurements are not available. Due to the limited information provided by a lidar system the reconstruction of shears in real-time is a challenging task especially for the horizontal shear in the presence of changing wind direction. The internal model principle has shown to be a promising approach to estimate the shears and directions in 10 minutes averages with real measurement data. The static model based wind vector field reconstruction is extended in this work taking into account a dynamic reconstruction model based on Taylor's Frozen Turbulence Hypothesis. The presented method provides time series over several seconds of the wind speed, shears and direction, which can be directly used in advanced optimal preview control. Therefore, this work is an important step towards the application of preview individual blade pitch control under realistic wind conditions. The method is tested using a turbulent wind field and a detailed lidar simulator. For the simulation, the turbulent wind field structure is flowing towards the lidar system and is continuously misaligned with respect to the horizontal axis of the wind turbine. Taylor's Frozen Turbulence Hypothesis is taken into account to model the wind evolution. For the reconstruction, the structure is discretized into several stages where each stage is reduced to an effective wind speed, superposed with a linear horizontal and vertical wind shear. Previous lidar measurements are shifted using again Taylor's Hypothesis. The wind field reconstruction problem is then formulated as a nonlinear optimization problem, which minimizes the residual between the assumed wind model and the lidar measurements to obtain the misalignment angle and the effective wind speed and the wind shears for each stage. This method shows good results in reconstructing the wind characteristics of a three dimensional turbulent wind field in real-time, scanned by a lidar system with an optimized trajectory.

Modern wind turbines operate in regimes where inflow variability and aeroelastic dynamics strongly affect energy capture and structural loading, motivating upstream preview sensing for proactive control. While nacelle-mounted Doppler lidar enables lidar-assisted control, its line-of-sight (LOS) measurements provide only partial information, limiting direct rotor-effective wind speed (REWS) estimation. This work implements a wind-field reconstruction framework based on an Unscented Kalman Filter (UKF) that fuses sparse LOS data with a reduced-order predictor derived from the 2D Navier–Stokes equations. Validation is performed against LES/CFD inflow fields generated for the NREL 5 MW turbine at 15 m/s. REWS from the reconstructed field reduces dispersion relative to a lidar-only baseline, and both preview routes are evaluated within the same Region-3 feedforward–feedback controller. Brute-force tuning of preview conditioning (cutoff frequency and buffer delay) shows that closed-loop benefits are primarily set by preview coherence and effective phase delay in the control-relevant band.

This work presents a lidar-assisted optimal feedforward control strategy for wind turbines. A nonlinear model predictive controller augments an existing industrial feedback controller with optimization-based feedforward control inputs, rather than replacing the baseline controller. In this way, the proposed approach preserves the validated closed-loop behavior and safety routines of the industrial feedback controller while adding objective-based tuning and explicit constraint handling. The controller optimizes the generator torque update and collective pitch rate as control inputs based on lidar-based wind preview. A reduced nonlinear wind turbine model is derived and utilized to formulate a constrained optimal control problem, which is solved efficiently using the open-source framework acados. The proposed controller is evaluated against the industrial feedback controller and a field-proven collective pitch feedforward controller in aero-elastic simulations using deterministic and stochastic wind fields. The results show that the optimal feedforward controller achieves robust compatibility with the existing industrial feedback controller. Reductions in actuator activity, rotor speed deviations and tower loads are produced relative to the feedback controller and the field-proven feedforward benchmark while slightly increasing the energy production. Furthermore, this work highlights that the achievable benefits strongly depend on the wind preview quality under realistic conditions.