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Wind farm flow control (WFFC) is a topic of interest at several research institutes and industry and certification agencies worldwide. For reliable performance assessment of the technology, the efficiency and the capability of the models applied to WFFC should be carefully evaluated. To address that, the FarmConners consortium has launched a common benchmark for code comparison under controlled operation to demonstrate its potential benefits, such as increased power production. The benchmark builds on available data sets from previous field campaigns, wind tunnel experiments, and high-fidelity simulations. Within that database, four blind tests are defined and 13 participants in total have submitted results for the analysis of single and multiple wakes under WFFC. Here, we present Part I of the FarmConners benchmark results, focusing on the blind tests with large-scale rotors. The observations and/or the model outcomes are evaluated via direct power comparisons at the upstream and downstream turbine(s), as well as the power gain at the wind farm level under wake steering control strategy. Additionally, wake loss reduction is also analysed to support the power performance comparison, where relevant. The majority of the participating models show good agreement with the observations or the reference high-fidelity simulations, especially for lower degrees of upstream misalignment and narrow wake sector. However, the benchmark clearly highlights the importance of the calibration procedure for control-oriented models. The potential effects of limited controlled operation data in calibration are particularly visible via frequent model mismatch for highly deflected wakes, as well as the power loss at the controlled turbine(s). In addition to the flow modelling, the sensitivity of the predicted WFFC benefits to the turbine representation and the implementation of the controller is also underlined. The FarmConners benchmark is the first of its kind to bring a wide variety of data sets, control settings, and model complexities for the (initial) assessment of farm flow control benefits. It forms an important basis for more detailed benchmarks in the future with extended control objectives to assess the true value of WFFC.

This paper describes the design and characterization of a scaled wind turbine model, conceived to support wake and wind farm control experiments in a boundary layer wind tunnel. The turbine has a rotor diameter of 0.6 m and was designed to match the circulation distribution of a target conceptual full-scale turbine at its design tip speed ratio. In order to enable the testing of plant-level control strategies, the model is equipped with pitch, torque and yaw actuation and is sensorized with integrated load cells, rotor azimuth and blade pitch encoders. After describing the design of the turbine, its steady-state performance and wake characteristics are assessed by conducting experiments in two different wind tunnels, in laminar and turbulent conditions, collecting wake data with different measurement techniques. A large-eddy simulator coupled to an actuator-line model is used to develop a digital replica of the turbine and of the wind tunnel. For increased accuracy, the polars of the low-Reynolds-number airfoil used in the numerical model are tuned directly from measurements obtained from the rotor in operation in the wind tunnel. Results indicate that the scaled turbine performs as expected: measurements are repeatable and consistent, and the wake appears to have a realistic behavior in line with expectations and with a similar but slightly larger scaled model turbine. Furthermore, the predictions of the numerical model are well in line with experimental observations.

A new method is described to identify the aerodynamic characteristics of blade airfoils directly from operational data of the turbine. Improving on a previously published approach, the present method is based on a new maximum likelihood formulation that includes both errors in the outputs and in the inputs, generalizing the classical error-in-the-outputs only formulation. Since many parameters are necessary to meaningfully represent the behavior of airfoil polars as functions of angle of attack and Reynolds number, the approach uses a singular value decomposition to solve for a reduced set of observable parameters. The new approach is demonstrated by identify high quality polars for small-scale wind turbines used in wind tunnel experiments for wake and wind farm control research.

Wind farm flow control (WFFC) is a topic of interest at several research institutes, industry and certification agencies world-wide. For reliable performance assessment of the technology, the efficiency and the capability of the models applied to WFFC should be carefully evaluated. To address that, FarmConners consortium has launched a common benchmark for code comparison under controlled operation to demonstrate its potential benefits such as increased power production. The benchmark builds on available data sets from previous field campaigns, wind tunnel experiments and high-fidelity simulations. Within that database, 4 blind tests are defined and 13 participants in total have submitted results for the analysis of single and multiple wake under WFFC. Some participants took part in several blind tests and some participants have implemented several models. The observations and/or the model outcomes are evaluated via direct power comparisons at the upstream and downstream turbine(s), as well as the power gain at the wind farm level under wake steering control strategy. Additionally, wake loss reduction is also analysed to support the power performance comparison, where relevant. Majority of the participating models show good agreement with the observations or the reference high-fidelity simulations, especially for lower degrees of upstream misalignment and narrow wake sector. However, the benchmark clearly highlights the importance of the calibration procedure for control-oriented models. The potential effects of limited controlled operation data in calibration is particularly visible via frequent model mismatch for highly deflected wakes, as well as the power loss at the controlled turbine(s). In addition to the flow modelling, sensitivity of the predicted WFFC benefits to the turbine representation and the implementation of the controller is also underlined. FarmConners benchmark is the first of its kind to bring a wide variety of data sets, control settings and model complexities for the (initial) assessment of farm flow control benefits. It forms an important basis for more detailed benchmarks in the future with extended control objectives to assess the true value of WFFC.

This paper describes the design and characterization of a scaled wind turbine model, conceived to support wake and wind farm control experiments in a boundary layer wind tunnel. The turbine has a rotor diameter of 0.6~meters, and was designed to match the circulation distribution of a target conceptual full-scale turbine at its design tip speed ratio. In order to enable the testing of plant-level control strategies, the model is equipped with closed-loop pitch, torque and yaw control, and is sensorized with integrated load cells, as well as with rotor azimuth and blade pitch encoders. After describing the design of the turbine, its performance and wake characteristics are assessed by conducting experiments in two different wind tunnels, in laminar and turbulent conditions, collecting wake data with different measurement techniques. A large-eddy simulator coupled to an actuator-line model is used to develop a digital replica of the turbine and of the wind tunnel. For increased accuracy, the polars of the low-Reynolds airfoil used in the numerical model are tuned directly from measurements obtained from the rotor in operation in the wind tunnel. Results indicate that the scaled turbine performs as expected, measurements are repeatable and consistent, and the wake appears to have a realistic behavior in line with expectations and with a similar slightly larger scaled model turbine. Furthermore, the predictions of the numerical model are well in line with experimental observations.

The aim of this paper is to analyze to which extent wind tunnel experiments can represent the behavior of full-scale wind turbine wakes. The question is relevant because on the one hand scaled models are extensively used for wake and farm control studies, whereas on the other hand not all wake-relevant physical characteristics of a full-scale turbine can be exactly matched by a scaled model. In particular, a detailed scaling analysis reveals that the scaled model accurately represents the principal physical phenomena taking place in the outer shell of the near wake, whereas differences exist in its inner core. A large eddy simulation actuator line method is first validated with respect to wind tunnel measurements, and then used to perform a detailed comparison of the wake at the two scales. It is concluded that, notwithstanding the existence of some mismatched effects, the scaled wake is remarkably similar to the full-scale one, except in the immediate proximity of the rotor.

A control scheme for drag power kites, also known as airborne wind turbines, for the entire wind speed range is proposed, including 1) a temperature controller allowing for temporary overloading of the powertrain; 2) a limitation controller ensuring that power, force, speed, and actuator constraints are satisfied; 3) a tangential flight speed controller; and 4) a tangential force control allocation, which inverts the nonline arities of the plant and allocates the flight speed controller’s tangential force demand to the available actuators. The drag power kite plant model is based on a point-mass model and a simple aerodynamics model with various drag contributions. Simulations are conducted with the parameters of the 20 kW Wing 7 developed by Makani Power, Inc. The proper working of the control scheme is indicated by the good match of the simulation results with independent simulation results and measurements published by Makani. A temporary overloading of the power train with about twice the nominal power can be concluded as a requirement; otherwise the mean power would be significantly lower. Because of the reduction of the lift and thus reduction of the centripetal force at high wind speeds, the inside-down figure eight can be concluded as the best pattern.

This paper compares the accuracy of four different FLORIS model implementations characterized by increasing levels of accounted physics. The predictions of each model were tested against experimental observations obtained from wind tunnel tests of a cluster of three scaled wind turbines. The experiments were conducted for a wide range of environmental conditions, and mainly with yawed turbines. The results showed a significant improvement in the accuracy of the base model by adding more details to the physical effects of the simulation, such as secondary steering and a heterogeneous flow field. It is also interesting to note that the FLORIS predictions become particularly accurate when considering inflow-dependent power losses in yaw-misaligned conditions.

In this study a new yaw mechanism for a small existing wind turbine model is designed and tested. The special requirement for this yaw device is its compact design due to the small length scaling factor of the model turbine. Such a dense design is crucial in order for the wake of the model turbine not to be influenced by the yaw mechanism. Furthermore, it has to have a fast system response and high accuracy to take into account the time scaling. Different concepts are investigated and a design located at the tower base is realized. To limit its drag, the structure is being concealed in an aerodynamically shaped cover. Wind tunnel measurements confirmed that the wake of the turbine is not influenced by the structure. A system analysis, using a Simulink model of the turbine helps finding the right motor and shows that the device has a fast system response with high accuracy. Consequently, the designed yaw mechanism is suited for the small wind turbine model, which can be used for experimental wake and wind farm control studies in the future.

The performance of an open-loop wake steering controller is investigated with a new unique set of wind tunnel experiments. A cluster of three scaled wind turbines, placed on a large turntable, is exposed to a turbulent inflow and dynamically changing wind directions, resulting in dynamically varying wake interactions. The changes in wind direction were sourced and scaled from a field-measured time history and mirrored on to the movement of the turntable. Exploiting the known, repeatable and controllable conditions of the wind tunnel, this study investigates the following effects: fidelity of the model used for synthesizing the controller, assumption of steady state vs. dynamic plant behavior, wind direction uncertainty, the robustness of the formulation in regard to this uncertainty, and a finite yaw rate. The results were analyzed for power production of the cluster, fatigue loads and yaw actuator duty cycle. The study highlights the importance of using a robust formulation and plant flow models of appropriate fidelity, and the existence of possible margins for improvement by the use of dynamic controllers.

As wind turbines in a wind farm interact with each other, a control problem arises that has been extensively studied in the literature: how can we optimize the power production of a wind farm as a whole? A traditional approach to this problem is called induction control, in which the power capture of an upstream turbine is lowered for the benefit of downstream machines. In recent simulation studies, an alternative approach, where the induction factor is varied over time, has shown promising results. In this paper, the potential of this dynamic induction control (DIC) approach is further investigated. Only periodic variations, where the input is a sinusoid, are studied. A proof of concept for this periodic DIC approach will be given by the execution of scaled wind tunnel experiments, showing for the first time that this approach can yield power gains in real-world wind farms. Furthermore, the effects on the damage equivalent loads (DEL) of the turbine are evaluated in a simulation environment. These indicate that the increase in DEL on the excited turbine is limited.

To account for the problem of an azimuthally constant induction in the BEM method, which influences on incorrectly predicted aerodynamic loads in the yawed flow, a skewed-wake model implementation to the BEM method has been performed. The numerical aerodynamic loads have been compared with the wind tunnel data of the NREL Phase VI and against another numerical campaign. At first, the model predictions have been validated against experimental data performed with aligned flow conditions, showing a reasonable match. Successively, the model predictions are validated against experimental results obtained with the wind turbine yawed. Results show, a possible better prediction of loads at yawed flow with Skewed-Wake correction, however the method does not overall correlate better, compared to the BEM method with implemented local variability of the induction factor.

Careful validation of the modelling and control actions is of vital importance to build confidence in the value of coordinated wind farm control (WFC). The efficiency of flow models applied to WFC should be evaluated to provide reliable assessment of the performance of WFC. In order to achieve that, FarmConners launches a common benchmark for code comparison to demonstrate the potential benefits of WFC, such as increased power production and mitigation of loads. The benchmark builds on available data sets from previous and ongoing campaigns: synthetic data from high-fidelity simulations, measurements from wind tunnel experiments, and field data from a real wind farm. The participating WFC models are first to be calibrated or trained using normal operation periods. For the blind test, both the axial induction and wake steering control approaches are included in the dataset and to be evaluated through the designed test cases. Three main test cases are specified, addressing the impact of WFC on single full wake, single partial wake, and multiple wake. The WFC model outcomes will be compared during the blind test phase, through power gain and wake loss reduction as well as alleviation of wake-added turbulence intensity and structural loads. The probabilistic validation will be based on the median and quartiles of the observations and WFC model predictions. Every benchmark participant will be involved in the final publication, where the comparison of different tools will be performed using the defined test cases. Instructions on how to participate are also provided on farmconners.readthedocs.io.

This paper describes a method to improve and correct an engineering wind farm flow model by using operational data. Wind farm models represent an approximation of reality and therefore often lack accuracy and suffer from unmodeled physical effects. It is shown here that, by surgically inserting error terms in the model equations and learning the associated parameters from operational data, the performance of a baseline model can be improved significantly. Compared to a purely data-driven approach, the resulting model encapsulates prior knowledge beyond the one contained in the training data set, which has a number of advantages. To assure a wide applicability of the method – including also to existing assets – learning is here purely driven by standard operational (SCADA) data. The proposed method is demonstrated first using a cluster of three scaled wind turbines operated in a boundary layer wind tunnel. Given that inflow, wakes and operational conditions can be precisely measured in the repeatable and controllable environment of the wind tunnel, this first application serves the purpose of showing that the correct error terms can indeed be identified. Next, the method is applied to a real wind farm situated in a complex terrain environment. Here again learning from operational data is shown to improve the prediction capabilities of the baseline model.

Simple demanded power point tracking (DPPT) control algorithms with a mode switch are proposed and experimentally validated in this study. One is a torque-based control and uses the generator torque with fixed blade-pitch angle. The other is blade-pitch based and uses both the generator torque and blade pitch for DPPT control. Both control algorithms receive power demand from a higherlevel controller and use their control strategies to track it. The two DPPT control algorithms were integrated with the basic torque and pitch control algorithms, and simulations using an in-house code and a high fidelity multi-body dynamic aeroelastic code were performed for steady and dynamic conditions. To verify the algorithms experimentally, wind tunnel tests with a scaled wind turbine having active pitch control capability were performed. From the simulation and the test, the proposed DPPT algorithms integrated into the conventional control algorithm were found to work well with the basic wind turbine power control algorithm. The torquebased control showed better performance in terms of power tracking, but the pitch-based control showed better performance in terms of loading.

This paper combines the research methodologies of scaled wind turbine model experiments in wind tunnels with short-range WindScanner lidar measurement technology. The wind tunnel at the Politecnico di Milano was equipped with three wind turbine models and two short-range WindScanner lidars to demonstrate the benefits of synchronised scanning lidars in such experimental surroundings for the first time. The dual-lidar system can provide fully synchronised trajectory scans with sampling timescales ranging from seconds to minutes. First, staring mode measurements were compared to hot-wire probe measurements commonly used in wind tunnels. This yielded goodness of fit coefficients of 0.969 and 0.902 for the 1 Hz averaged u and v components of the wind speed, respectively, validating the 2-D measurement capability of the lidar scanners. Subsequently, the measurement of wake profiles on a line as well as wake area scans were executed to illustrate the applicability of lidar scanning to the measurement of small-scale wind flow effects. An extensive uncertainty analysis was executed to assess the accuracy of the method. The downsides of lidar with respect to the hot-wire probes are the larger measurement probe volume, which compromises the ability to measure turbulence, and the possible loss of a small part of the measurements due to hard target beam reflection. In contrast, the benefits are the high flexibility in conducting both point measurements and area scanning and the fact that remote sensing techniques do not disturb the flow during measuring. The research campaign revealed a high potential for using short-range synchronised scanning lidars to measure the flow around wind turbines in a wind tunnel and increased the knowledge about the corresponding uncertainties.

While it has long been a practice to place spires near the inlet of a wind tunnel to quickly develop a turbulent boundary layer with similarities to an atmospheric boundary layer, this has not been the case for creating turbulent boundary layer inflow in large eddy simulations (LESs) of turbulent flows. We carry out LES with the curvilinear immersed boundary method to simulate the flow in a wind tunnel with a series of spires in order to investigate the feasibility of numerically developing inflow conditions from a precursory spire LES and assessing the similarities of the turbulence statistics to those of an atmospheric boundary layer. The simulated mean velocity field demonstrates that a turbulent boundary layer with height equal to the spire height develops very quickly, within five spire heights downstream. The major attribute of using spires for precursory simulations is the spatially evolving coherent structures that form downstream of the spires offering a range of length scales at both the...

Wind energy has developed worldwide, becoming a mature technology. Nevertheless, major advances regarding wind turbine aerodynamics and control as well as wind farm control and its integration in the power grid are being foreseen in the near future. To accomplish that, different simulation tools have emerged. The Actuator Line Model (ALM) in the frame of Large Eddy Simulation method has been used to study different related topics, from wake stability to wind turbine interaction and wind farm control, and it is considered the state of the art to simulate with high fidelity the wind flow through wind turbines and wind farms. Despite that, the ALM results, particularly loads and power production, have shown to be dependent on the numerical setup, like the projection function and its smearing parameter. The present paper aims to contribute to the latter by showing the influence of limiting the projection region. It is found that projecting the aerodynamic forces onto a cylinder that confines the rotor disk improves the computed power and loads compared to a Blade Element Momentum code and affects the wake development.

Dynamic Induction Control (DIC) has the potential of boosting wind farm power by enhancing wake recovery, whereby periodic pitch motions are used to exploit the natural instabilities of wind turbine wakes. This work studies DIC both experimentally and numerically. A thorough validation of an LES-ALM (Actuator Line Method) simulation tool is first conducted against experimental measurements. This shows that the CFD model is able to accurately simulate the power, loads and wake behavior of a wind turbine operating with DIC. The validated CFD model is then employed to study the effects of some DIC parameters. Results indicate an increase in the fatigue loads caused by the pitch activity that enhances wake recovery.

This paper aims at quantifying and explaining the effects of Individual Pitch Control (IPC) on the wake of a wake-steering wind turbine. As the machine is intentionally misaligned with respect to the wind, IPC can be used to mitigate the resulting extra loading. However, while IPC reduces loads, it also affects the wake, which influences the power of downstream turbines. The question is therefore whether the IPC activity has any other appreciable effect at the wind farm level, in addition to its original turbine-level load reduction goal. In this work, experiments and CFD simulations of scaled wind turbines in a boundary layer wind tunnel are considered. The CFD model is first validated with measurements, and then used to show subtle changes in the wake, power and loads caused by IPC. It is observed that IPC does indeed have some non-negligible effects, and that these effects differ for positive and negative yawing.

The impact of wake steering control strategy on power production and loads for a cluster of two wind turbines is analysed in this work. The power and loads data base are generated by means of high resolution simulations using an ALM-LES model along with an aeroelastic code for multibody analysis, while the optimal yaw misalignment values are obtained, for different wind directions and performance requirements, using a weighted cost function that accounts for both power production and fatigue damage on main wind turbine components. The sensitivity of the results regarding the inflow conditions, in terms of turbulence intensity, is also analysed. The overall results show that when accounting for both power production and fatigue damage, greater values of optimal yaw misalignment are obtained. This indicates that standard yaw misalignment strategies that accounts only for power production reduce at the same time the loading damage on the wind turbine, compared to greedy configuration.

In this paper a comparison between the wake velocity, wake deflection and turbulence intensity predictions of several wake models was carried out against wind tunnel data obtained with a state-of-the-art scaled wind turbine model. In order to achieve a fair comparison, the models’ parameters were all tuned with respect to the same experimental dataset using the Maximum Likelihood Estimation (MLE) method. A quantitative assessment of all models’ predictions highlighted that the Porté-Agel model seems to provide, for a wide range of inflow and wind turbine operating conditions, the most accurate estimation of the wake flow field. Further improvements to the model are also suggested in the conclusions.

This paper presents a modified version of the Ainslie eddy viscosity wake model and its accuracy by comparing it with selected exiting wake models and wind tunnel test results. The wind tunnel test was performed using a 1.9 m rotor diameter wind turbine model operating at a tip speed ratio similar to that of modern megawatt wind turbines. The control algorithms for blade pitch and generator torque used for below and above rated wind speed regions similar to those for multi-MW wind turbines were applied to the scaled wind turbine model. In order to characterize the influence of the wind turbine operating conditions on the wake, the wind turbine model was tested in both below and above rated wind speed regions at which the thrust coefficients of the rotor varied. The correction of the Ainslie eddy viscosity wake model was made by modifying the empirical equation of the original model using the wind tunnel test results with the Nelder-Mead simplex method for function minimization. The wake prediction accuracy of the modified wake model in terms of wind speed deficit was found to be improved by up to 6% compared to that of the original model. Comparisons with other existing wake models are also made in detail.

This paper first describes a large-eddy simulation approach, and then verifies it with respect to single-turbine wind tunnel experiments. Various aspects of the numerical approach are considered, to try to reduce its need for tuning, improve its accuracy and limit its computational cost. Simulation results are compared to measurements, including rotor and wake quantities. The study includes normal operating conditions, as well as wake manipulation by derating, yaw misalignment and cyclic pitching of the blades. Results indicate a good overall matching of simulations with experiments. Low turbulence test cases appear to be more challenging than moderate and high turbulence ones, due to the need for denser grids to limit numerical diffusion and accurately resolve tip-shed vortices in the near wake region.

Wake steering by active yawing of upstream wind turbines is a promising wind plant control technique. To enable the development of model-based wind plant control methods, there is a need for models that can marry the contrasting requirements of good fidelity and low computational cost. This paper presents a reduced-order model (ROM) obtained by directly compressing high-fidelity computational fluid dynamics (CFD) simulation data using the proper orthogonal decomposition (POD) method. At first, simulations of wake-interacting wind turbines are obtained for time-varying yaw settings using the lifting-line large-eddy simulation (LES) code SOWFA. Next, a ROM is synthesized from the CFD transient simulations, obtaining a discrete-time state-space model that captures the dominant dynamics of the underlying high-fidelity model with only a reduced number of states. The ROM is optionally augmented with a Kalman filter, which feeds back turbine power measurements from the plant to the model, enhancing its accuracy. Results obtained in realistic turbulent conditions show a good agreement between high-fidelity CFD solutions and the proposed POD-based ROM in terms of wake behavior and power output of waked turbines. Additionally, the ROM presents acceptable results whencompared to wind tunnel experiments, including the capability of the model to partially correct for an intentionally built-in model mismatch.

We present wind tunnel and numerical experiments of the flow behind a model wind turbine in optimal and sub-optimal regimes. Spatio-temporal filtering and dynamic mode decomposition reveal that the onset and amplitude of wake meandering are affected by both the operating regime and the nacelle.

This paper describes the methodology that was followed for designing a scaled wind turbine model. This model is intended to be used in complex terrain studies as well as deep array wind farm control tests. Therefore, emphasis was given on making it as compact as possible while keeping a high level of instrumentation. The result is a three blade wind turbine with rotor diameter of 0.6 m equipped with active pitch, yaw and torque control. After a discussion on the design procedure, we present preliminary performance characteristics of the model obtained experimentally.

In this paper an adaptation of the FLORIS approach is considered that models the wind flow and power production within a wind farm. In preparation to the use of this model for wind farm control, this paper considers the problem of its calibration and validation with the use of experimental observations. The model parameters are first identified based on measurements performed on an isolated scaled wind turbine operated in a boundary layer wind tunnel in various wind-misalignment conditions. Next, the wind farm model is verified with results of experimental tests conducted on three interacting scaled wind turbines. Although some differences in the estimated absolute power are observed, the model appears to be capable of identifying with good accuracy the wind turbine misalignment angles that, by deflecting the wake, lead to maximum power for the investigated layouts.

A method to detect wake impingement on a wind turbine is described. The wake detector, which uses rotor loads as measured by on-board sensors, can then be used for implementing wind farm control strategies, for example in the form of wake redirection. The method is verified and validated by using field test data, aeroservoelastic simulations and by experimental data obtained with scaled wind turbine models tested in a large boundary layer wind tunnel.

This paper focuses on wake redirection techniques for wind farm control. Two control strategies are investigated: yaw misalignment and cyclic pitch control. First, analytical formulas are derived for both techniques, with the goal of providing a simple physical interpretation of the behavior of the two methods. Next, more realistic results are obtained by numerical simulations performed with CFD and by experiments conducted with scaled wind turbine models operating in a boundary layer wind tunnel. Comparing the analytical, numerical and experimental models allows for a cross-validation of the results and a better understanding of the two wake redirection techniques. Results indicate that yaw misalignment is more effective than cyclic pitch control in displacing the wake laterally, although the latter may have positive effects on wake recovery.

This paper presents results from wind tunnel tests aimed at evaluating a closed- loop wind farm controller for wind farm power maximization by wake deflection. Experiments are conducted in a large boundary layer wind tunnel, using three servo-actuated and sensorized wind turbine scaled models. First, we characterize the impact on steady-state power output of wake deflection, achieved by yawing the upstream wind turbines. Next, we illustrate the capability of the proposed wind farm controller to dynamically driving the upstream wind turbines to the optimal yaw misalignment setting.

This paper combines the currently relevant research methodologies of scaled wind turbine model experiments in wind tunnels with remote-sensing short-range WindScanner Lidar measurement technology. The wind tunnel of the Politecnico di Milano was equipped with three wind turbine models and two short-range WindScanner Lidars to demonstrate the benefits of synchronised scanning Lidars in such experimental surroundings for the first time. The dual- Lidar system can provide fully synchronised trajectory scans with sampling time scales ranging from seconds to minutes. First, staring mode measurements were compared to hot wire probe measurements commonly used in wind tunnels. This yielded goodness of fit coefficients of 0.969 and 0.902 for the 1 Hz averaged u- and v-components of the wind speed, respectively, validating the 2D measurement capability of the Lidar scanners. Subsequently, the measurement of wake profiles on a line as well as wake area scans were executed to illustrate the applicability of Lidar scanning to measuring small scale wind flow effects. The downsides of Lidar with respect to the hot wire probes are the larger measurement probe volume and the loss of some measurements due to moving blades. In contrast, the benefits are the high flexibility in conducting both point measurements and area scanning, and the fact that remote sensing techniques do not disturb the flow while measuring. The research campaign revealed a high potential for using short-range WindScanner Lidar for accurately measuring small scale flow structures in a wind tunnel.

The paper describes a simple method for detecting presence and location of a wake affecting a downstream wind turbine operating in a wind power plant. First, the local wind speed and shear experienced by the wind turbine are estimated by the use of rotor loads and other standard wind turbine response data. Then, a simple wake deficit model is used to determine the lateral position of the wake with respect to the affected rotor. The method is verified in a boundary layer wind tunnel using two instrumented scaled wind turbine models, demonstrating its effectiveness.