Wind Field Reconstruction from Nacelle-Mounted Lidars Short Range Measurements

Profiling nacelle lidars probe the wind at several heights and several distances upstream of the rotor. The development of such lidar systems is relatively recent, and it is still unclear how to condense the lidar raw measurements into useful wind field characteristics such as speed, direction, vertical and longitudinal gradients (wind shear). In this paper, we demonstrate an innovative method to estimate wind field characteristics using nacelle lidar measurements taken within the induction zone. Model-fitting wind field reconstruction techniques are applied to nacelle lidar measurements taken at multiple distances close to the rotor, where a wind model is combined with a simple induction model. The method allows robust determination of free-stream wind characteristics. The method was applied to experimental data obtained with two different types of nacelle lidar (five-beam Demonstrator and ZephIR Dual Mode). The reconstructed wind speed was within 0.5 % of the wind speed measured with a mast-top-mounted cup anemometer at 2.5 rotor diameters upstream of the turbine. The technique described in this paper overcomes measurement range limitations of the currently available nacelle lidar technology

Correlation-model of rotor-effective wind shears and wind speed for lidar-based individual pitch control

In this work the spectra based model of the correlation between lidar systems and wind turbines is extended from rotor-effective wind speed only, to rotor-effective wind speed and linear horizontal and vertical shear components. This is achieved by the incorporation of a model based wind field reconstruction method solving a set of linear equations with the least-squares method. The model allows to optimize a lidar system’s measurement configuration for a specific wind turbine a-priori by means of direct and fast spectra calculations. Furthermore, it allows to assess the filter parameters to be expected and needed for the application of lidar-assisted control. By extending the model to rotor-effective linear shears, the results can be used for lidar-assisted individual pitch control

Realistic simulations of extreme load cases with lidar-based feedforward control

This work presents the development of a simulation environment which allows to simulate realistic extreme events with lidar-based feedforward control. This environment includes turbulent wind fields including extreme events, wind evolution and wind field scanning with a nacelle-based lidar system. It is designed to simulate lidar-based controllers in a realistic environment. In addition, a controller extension is proposed to identify and mitigate extreme events in wind fields based on lidar measurements. The combination of this extreme event controller with the realistic simulation environment is a promising tool for load reductions in wind turbines

Model based wind vector field reconstruction from lidar data

In recent years lidar technology found its way into wind energy for resource assessment and control. For both fields of application it is crucial to reconstruct the wind field from the limited information provided by a lidar system. For lidar assisted wind turbine control model based wind field reconstruction is used to obtain signals from wind characteristics such as wind speed, direction and shears in a high temporal resolution. This work shows how these methods can be used for lidar based wind resource assessment in complex situations, where high accuracy is important, but cannot be archived by conventional technique. The reconstruction is validated for ground based lidar systems with measurement data and for floating lidar systems with detailed simulations

Field testing of feedforward collective pitch control on the CART2 using a nacelle-based lidar scanner

This work presents the first results from a field test to proof the concept of LIDAR assisted collective pitch control using a scanning LIDAR device installed on the nacelle of a research turbine. The purpose of the campaign was to show that a reduction of rotor speed variation is feasible with a feedforward update without changing the feedback controller. Although only a small amount of data could be collected, positive effects can be observed not only on the rotor speed but also on tower, blade and shaft loads in the case that the correlation of the wind preview and the turbine reaction is taken into account

Direct speed control using LIDAR and turbine data

LIDAR systems are able to provide preview information of the wind speed in front of wind turbines. One proposed use of this information is to increase the energy capture of the turbine by adjusting the rotor speed directly to maintain operation at the optimal tip-speed ratio, a technique referred to as Direct Speed Control (DSC). Previous work has indicated that for large turbines the marginal benefit of the direct speed controller in terms of increased power does not compensate for the increase of the shaft loads. However, the technique has not yet been adequately tested to make this determination conclusively. Further, it is possible that applying DSC to smaller turbines could be worthwhile because of the higher rotor speed fluctuations and the small rotor inertia. This paper extends the previous work on direct speed controllers. A DSC is developed for a 600 kW experimental turbine and is evaluated theoretically and in simulation. Because the actual turbine has a mounted LIDAR, data collected from the turbine and LIDAR during operation are used to perform a hybrid simulation. This technique allows a realistic simulation to be performed, which provides good agreement with theoretical predictions

An adaptive data processing technique for lidar-assisted control to bridge the gap between lidar systems and wind turbines

This paper presents first steps toward an adaptive lidar data processing technique crucial for lidar-assisted control in wind turbines. The prediction time and the quality of the wind preview from lidar measurements depend on several factors and are not constant. If the data processing is not continually adjusted, the benefit of lidar-assisted control cannot be fully exploited or can even result in harmful control action. An online analysis of the lidar and turbine data is necessary to continually reassess the prediction time and lidar data quality. In this work, a structured process to develop an analysis tool for the prediction time and a new hardware setup for lidar-assisted control are presented. The tool consists of an online estimation of the rotor effective wind speed from lidar and turbine data and the implementation of an online cross-correlation to determine the time shift between both signals. Further, we present initial results from an ongoing campaign in which this system was employed for providing lidar preview for feedforward pitch control

Optimization of a feed-forward controller using a CW-lidar system on the CART3

This work presents results from a new field-testing campaign conducted on the three-bladed Controls Advanced Research Turbine (CART3) at the National Renewable Energy Laboratory in 2014. Tests were conducted using a commercially available, nacelle-mounted continuous-wave lidar system from ZephIR Lidar for the implementation of a lidar-based collective pitch feed-forward controller. During the campaign, the data processing of the lidar system was optimized for higher availability. Furthermore, the optimal scan distance was investigated for the CART3 by means of a spectra-based analytical model and found to match the lidar's capabilities well. Throughout the campaign the predicted correlation between the lidar measurements and the turbine's reaction was confirmed from the measured data. Additionally, the baseline feedback controller's gains were tuned based on a simulation study that included the lidar system to achieve further load reductions. This led to some promising first results, which are presented at the end of this paper

Optimizing Lidars for Wind Turbine Control Applications—Results from the IEA Wind Task 32 Workshop

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