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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.

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 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.

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.

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.

Goal of this paper is to present results from wind tunnel tests aimed at evaluating the potential of different wake control strategies for wind farm power maximization and load reduction. The experiments are conducted in a large boundary layer wind tunnel, using up to six servo-actuated and highly sensorized wind turbine scaled models in different wind farm layouts. Two main strategies are considered: the first derates the upstream wind turbines, while the second aims at redirecting wakes away from the downstream machines. The latter strategy is implemented in two alternative ways, using either active yawing or individual blade pitching. The impact on wind farm power and loading of these control strategies is discussed, highlighting the most promising ones.

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.

In this study, the effect of active induction control of upstream wind turbines is investigated. Two scaled wind turbines having a rotor diameter of 1 m with a spacing of four times of the rotor diameter were used to experimentally validate the concept. Also, an in-house c code was used to simulate the same two wind turbines and see if the experimental observations can be obtained. From the experiment, approximately 0.81% increase of total power could be observed. Although the simulation results were not exactly the same as the experimental results but the shape was similar and the maximum power increase of 0.27% was predicted. Also from further simulation using NREL 5MW wind turbines instead of scaled wind turbines with realistic ambient turbulence intensity, it was found that the power increase could become more than 1%.

As an alternative to conventional wind turbines, this study considered kites with onboard wind turbines driven by a high airspeed due to crosswind flight (“drag power”). The hypothesis of this study was, that if the kite's lift coefficient is maximized, then the power, energy yield, allowed costs and profit margin are also maximized. This hypothesis was confirmed based on a kite power system model extended from Loyd's model. The performance of small-scale and utility-scale kites in monoplane and biplane configurations were examined for increasing lift coefficients. Moreover, several parameters of the utility-scale system were optimized with a genetic algorithm. With an optimal lift coefficient of 4.5, the biplane outperformed the monoplane. A 40 m wing span kite was expected to achieve a rated power of about 4.1 MW with a power density of about 52 kW/m2. A parameter sensitivity analysis of the optimized design was performed. Moreover, to demonstrate the feasibility of very high lift coefficients and the validity of a utilized simplified airfoil polar model, CFDs of a proposed high-lift multi-element airfoil were performed and the airfoil polars were recorded. Finally, a planform design of a biplane kite was proposed.

The aim of this work is to present a detailed code-to-code comparison of two Large-Eddy Simulation (LES) solvers. Corresponding experimental measurements are used as a reference to validate the quality of the CFD simulations. The comparison highlights the effects of solver order on the solutions, and it tries to answer the question of whether a high order solver is necessary to capture the main characteristics of a wind farm. Both solvers were used on different grids to study their convergence behavior. While both solvers show a good match with experimental measurements, it appears that the low order solver is more accurate and substantially cheaper in terms of computational cost.