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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.

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.

Ensuring safe operation at all times and in all conditions is one of the primary goals of wind turbine control systems. This paper presents a novel approach for wind turbine control based on the concept of envelope riding. The proposed approach utilizes on-line numerical optimization to predict at each time instant the extremal wind speed that would lead the machine to encounter the envelope of its safe operating range. Based on this extremal condition, control inputs are computed that maintain the response within the safe region at all times, at the most riding its boundary but without ever leaving it. The method is capable of keeping the machine within its safe envelope within the entire range of operating wind speeds, including both rated and cut-out conditions. The new method is verified with the help of numerical simulations conducted with a high-fidelity aeroservoelastic environment. Comparisons are made with standard control algorithms for the prevention of excessive loading, including peak-shaving and soft cut-out schemes. Results illustrate the ability of the proposed approach in reducing loads and improving power output.

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.

The paper describes the development of a scaled model of complex terrain, suitable for terrain-wind turbine interaction wind tunnel studies, taking into account flow similarity criteria. The size and the geometry of the experimental model of the complex terrain were refined using results of CFD simulations in order to achieve the best possible flow similarity and avoid edge effects arising from the finite (relative to the rotor size) terrain geometry. Moreover, Particle Image Velocimetry was used to survey the flow field on a longitudinal plane along the terrain center line. Flow measurements with and without a wind turbine model enabled to quantitatively evaluate the speed up produced by the terrain in the region of the wind turbine and the effect of the terrain on the wake characteristics of the wind turbine model.

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.

Wind farm control is an active and growing field of research in which the control actions of individual turbines in a farm are coordinated, accounting for inter-turbine aerodynamic interaction, to improve the overall performance of the wind farm and to reduce costs. The primary objectives of wind farm control include increasing power production, reducing turbine loads, and providing electricity grid support services. Additional objectives include improving reliability or reducing external impacts to the environment and communities. In 2019, a European research project (FarmConners) was started with the main goal of providing an overview of the state-of-the-art in wind farm control, identifying consensus of research findings, data sets, and best practices, providing a summary of the main research challenges, and establishing a roadmap on how to address these challenges. Complementary to the FarmConners project, an IEA Wind Topical Expert Meeting (TEM) and two rounds of surveys among experts were performed. From these events we can clearly identify an interest in more public validation campaigns. Additionally, a deeper understanding of the mechanical loads and the uncertainties concerning the effectiveness of wind farm control are considered two major research gaps.

This study explores the potential benefits of considering Lidar-assisted control (LAC) at the first stages of wind turbine design. The proposed methodology starts with a load analysis of several reference wind turbines to understand which design constraints can be influenced by the use of LAC. The blade and tower of each analyzed model are redesigned considering LAC-induced reductions in key driving quantities. Preliminary results suggest modest reductions in LCOE with potentially significant benefits limited to the tower. The study also discusses the requirements on LAC system purchase and O&M costs, for both onshore and offshore machines, to achieve a reduction in LCOE.

This chapter presents models and methods for the automated design optimization of wind turbine rotors, possibly with a somewhat skewed view toward the approaches developed by the authors. This discipline is relatively new and is expected to quickly evolve in the years to come, spurred by a need to support ever more challenging designs. The material presented in this chapter is intimately related with Chapter 7 on systems engineering. Indeed, the design of the rotor is strictly connected with the overall design of the machine and of its various components

The present paper characterizes the performance of non-intrusive uncertainty quantification methods for aeroservoelastic wind turbine analysis. Two different methods are considered, namely non-intrusive polynomial chaos expansion and Kriging. Aleatory uncertainties are associated with the wind inflow characteristics and the blade surface state, on account of soiling and/or erosion, and propagated throughout the aeroservoelastic model of a large conceptual offshore wind turbine. Results are compared with a brute-force extensive Monte Carlo sampling, which is used as benchmark. Both methods require at least 1 order of magnitude less simulations than Monte Carlo, with a slight advantage of Kriging over polynomial chaos expansion. The analysis of the solution space clearly indicates the effects of uncertainties and their couplings, and highlights some possible shortcomings of current mostly deterministic approaches based on safety factors.

The standard material fatigue estimation process is implemented in an Economic Nonlinear Model Predictive Controller. The process comprises cycle identification, mean-stress effect, Wohler curves and Linear Damage Accumulation. For cycle identification, this novel implementation uses a standard Rainflow-algorithm which is substituted periodically by linear functions based on its output. The implementation is compared to two straightforward methods for fatigue penalization using a wind turbine model and exhibits higher profit over the entire range of tested reference wind velocities.

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.

This paper discusses the idea of vertical wake deflection for offshore wind turbines using active ballast control. First, the concept of active ballast control for changing the attitude of an offshore wind energy semi-submersible platform is presented and briefly explored. Next, the influence of vertical wake deflection on the wake behavior and power production of a cluster of two wind turbines is studied through wind tunnel experiments and LES simulations. Results show that vertical deflection changes substantially the wake characteristics and affects the inflow at a downstream wind turbine in terms of available kinetic energy and turbulence intensity. For the most favorable case considered here, the cluster power was increased by approximately 8%.

A multifaceted future for wind power Modern wind turbines already represent a tightly optimized confluence of materials science and aerodynamic engineering. Veers et al. review the challenges and opportunities for further expanding this technology, with an emphasis on the need for interdisciplinary collaboration. They highlight the need to better understand atmospheric physics in the regions where taller turbines will operate as well as the materials constraints associated with the scale-up. The mutual interaction of turbine sites with one another and with the evolving features of the overall electricity grid will furthermore necessitate a systems approach to future development. Science , this issue p. eaau2027

In this work, a new algorithm is presented to correct for pitch misalignment imbalances of wind turbine rotors. The method uses signals measured in the fixed frame of the machine, typically in the form of accelerations or loads. The amplitude of the one per revolution signal harmonic is used to quantify the imbalance, while its phase to locate the unbalanced blade(s). The near linearity of the unknown relationship between harmonic amplitude and pitch misalignment is used to derive a simple algorithm that iteratively rebalances the rotor. This operation is conducted while the machine is in operation, without the need of shutting it down. The method is not only applicable to the case of a single misaligned blade, but also to the generic case of multiple concurrent imbalances. A part from the availability of acceleration or load sensors, the method requires the ability of the rotor blades to be commanded independently from one another, which is typically possible on many modern machines. The new method is demonstrated in a realistic simulation environment, using an aeroservoelastic wind turbine model in a variety of wind and operating conditions.

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.

The size of wind turbines has been steadily growing in the pursuit of a lower cost of energy by an increased wind capture. In this trend, the vast majority of wind turbine rotors has been designed based on the conventional three-bladed upwind concept. This paper aims at assessing the optimality of this configuration with respect to a three-bladed downwind design, with and without an actively controlled variable coning used to reduce the cantilever loading of the blades. A 10 MW wind turbine is used for the comparison of the various design solutions, which are obtained by an automated comprehensive aerostructural design tool. Results show that, for this turbine size, downwind rotors lead to blade mass and cost reductions of 6 % and 2 %, respectively, compared to equivalent upwind configurations. Due to a more favorable rotor attitude, the annual energy production of downwind rotors may also slightly increase in complex terrain conditions characterized by a wind upflow, leading to an overall reduction in the cost of energy. However, in more standard operating conditions, upwind rotors return the lowest cost of energy. Finally, active coning is effective in alleviating loads by reducing both blade mass and cost, but these potential benefits are negated by an increased system complexity and reduced energy production. In summary, a conventional design appears difficult to beat even at these turbine sizes, although a downwind non-aligned configuration might result in an interesting alternative.

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.

The present paper further develops and experimentally validates the previously published idea of estimating the wind inflow at a turbine rotor disk from the machine response. A linear model is formulated that relates one per revolution (1P) harmonics of the in- and out-of-plane blade root bending moments to four wind parameters, representing vertical and horizontal shears and misalignment angles. Improving on this concept, the present work exploits the rotationally symmetric behaviour of the rotor in the formulation of the load-wind model. In a nutshell, this means that the effects on the loads of the vertical shear and misalignment are the same as those of the horizontal quantities, simply shifted by π / 2. This results in a simpler identification of the model, which needs a reduced set of observations. The performance of the proposed method is first tested in a simulation environment and then validated with an experimental data set obtained with an aeroelastically scaled turbine model in a boundary layer wind tunnel.

This paper is concerned with the holistic optimization of wind turbines. A multi-disciplinary optimization procedure is presented that marries the overall sizing of the machine in terms of rotor diameter and tower height (often termed “preliminary design”) with the detailed sizing of its aerodynamic and structural components. The proposed combined preliminary–detailed approach sizes the overall machine while taking into full account the subtle and complicated couplings that arise due to the mutual effects of aerodynamic and structural choices. Since controls play a central role in dictating performance and loads, control laws are also updated accordingly during optimization. As part of the approach, rotor and tower are sized simultaneously, even in this case capturing the mutual effects of one component over the other due to the tip clearance constraint. The procedure, here driven by detailed models of the cost of energy, results in a complete aero-structural design of the machine, including its associated control laws. The proposed methods are tested on the redesign of two wind turbines, a 2.2 MW onshore machine and a large 10 MW offshore one. In both cases, the optimization leads to significant changes with respect to the initial baseline configurations, with noticeable reductions in the cost of energy. The novel procedures are also exercised on the design of low-induction rotors for both considered wind turbines, showing that they are typically not competitive with conventional high-efficiency rotors.

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.

The European Academy of Wind Energy (eawe), representing universities and institutes with a significant wind energy programme in 14 countries, has discussed the long-term research challenges in wind energy. In contrast to research agendas addressing short- to medium-term research activities, this eawe document takes a longer-term perspective, addressing the scientific knowledge base that is required to develop wind energy beyond the applications of today and tomorrow. In other words, this long-term research agenda is driven by problems and curiosity, addressing basic research and fundamental knowledge in 11 research areas, ranging from physics and design to environmental and societal aspects. Because of the very nature of this initiative, this document does not intend to be permanent or complete. It shows the vision of the experts of the eawe, but other views may be possible. We sincerely hope that it will spur an even more intensive discussion worldwide within the wind energy community.

An engineering wake model is updated online based on measurements provided by shaded turbines. Departing from other approaches, the measurements include information on the impinging wakes, obtained by a wake detector based on measured rotor loads. The updated model exhibits improved prediction capabilities, and it can be used for implementing a model-based wind farm controller.

This study integrates aeroacoustic noise emission models within a wind turbine design procedure to include overall sound pressure levels as design constraints. The proposed approach aims at the minimization of the cost of energy from wind, while ensuring the compliance with noise emission limits. The reference 3.35 MW onshore wind turbine developed within the international cooperation IEA Wind Task 37 is redesigned to reduce its noise emissions above and below rated wind speed, considering both single- and multi-objective design criteria. Results obtained with the proposed noise-constrained redesign methodology are compared with the simpler approach of reducing the tip speed without altering the blade shape. Results show that, while the simplistic approach causes a drop of −2.8% in annual energy production and a +2.5% increase in cost of energy, an optimized configuration fulfills the noise requirement without incurring into significant energy penalties.

Wind condition awareness is an important factor to maximize power extraction, reduce fatigue loading and increase the power quality of wind turbines and wind power plants. This paper presents a new method for wind speed estimation based on blade load measurements. Starting from the definition of a cone coefficient, which captures the collective zeroth-harmonic of the out-of-plane blade bending moment, a rotor-effective wind speed estimator is introduced. The proposed observer exhibits a performance similar to the well known torque balance estimator. However, while the latter only measures the average wind speed over the whole rotor disk, the proposed approach can also be applied locally, resulting in estimates of the wind speed in different regions of the rotor disk. In the present work, the proposed method is used to estimate the average wind speed over four rotor quadrants. The top and bottom quadrants are used for estimating the vertical shear profile, while the two lateral ones for detecting the presence of a wake shed by an upstream wind turbine. The resulting wake detector can find applicability in wind farm control, by indicating on which side of the rotor the upstream wake is impinging. The new approach is demonstrated with the help of field test data, as well as simulations performed with high-fidelity aeroservoelastic models.

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.

We use the capabilities of a multi-disciplinary design tool to provide a definition of a 20 MW wind turbine. Starting from an aero-elastic model obtained through a classic scaling procedure, we conduct an aero-structural optimization of the rotor through a staged redesign process, in which we optimize primary characteristics of the rotor including the blade shape, the solidity and a certain amount of native structural tailoring. The process is based on a series of parametric analysis, in order to assess the impact of a variation of macro design parameters on the fundamental performance of the turbine. The redesign activity shows remarkable advantages in terms of blade mass reduction and load alleviation, highlighting directions for the development and optimization of very large rotors.

In this work, a control-oriented wind farm model is validated against SCADA data from a typical on-shore wind farm, without additional instrumentation available. The comparison of model-predicted and measured power decits due to wake impingement shows good agreement. Furthermore, the model is used to compute optimum yaw misalignments for yaw-induced wake steering, leading to an estimated 1.7% increase in annual energy production by mitigation of wake losses. Results show that wake steering based on standard SCADA data, which is usually available in operational wind farms, has promising potential for open-loop model-based wind farm control.

This work reports preliminary findings on the field validation of the “rotor as a sensor” concept. In a nutshell, the idea is to use the rotor response in order to measure the wind inflow, effectively turning the whole rotor into a sort of generalized anemometer capable of estimating wind speed, shears and directions. In turn, these quantities can be used for optimizing turbine and farm-level performance. Here a purely data-driven method is used, where a wind observer is first identified using flow field measurements (obtained by a met-mast) together with the corresponding blade root load harmonics. Once the observer has been identified, the wind inflow is estimated online during turbine operation by feeding it with measured blade root harmonics. Preliminary results reported herein seem to be very encouraging.

In this work, a new method is proposed for the stability analysis of wind turbines. The method uses input–output time histories obtained by conducting virtual excitation experiments with a suitable wind turbine simulation model. Next, a single-input/single-output periodic reduced model is identified from the recorded response and used for a stability analysis conducted according to the Floquet theory. Since only input–output sequences are used, the approach is model independent in the sense that it is applicable to wind turbine simulation models of arbitrary complexity. The use of the Floquet theory reveals a much richer picture than the one obtained by widespread classical approaches based on the use of the multi-blade coordinate transformation of Coleman. In fact, it is shown here that, for each principal mode computed by the classical approach, there are in reality infinite super-harmonics of varying strength fanning out from the principal one at multiples of the rotor speed. The relative strength of each harmonic in a fan provides for a way of measuring how periodically one specific fan of modes behaves. The notion of super-harmonics allows one to justify the presence of peaks in the response spectra, peaks that cannot be explained by the classical time-invariant analysis. The Campbell diagram, i.e., the plot of system frequencies vs. rotor speed, is in this work enriched by the presence of the super-harmonics, revealing a much more complex pattern of possible resonant conditions with the per-rev excitations than normally assumed. Copyright © 2014 John Wiley & Sons, Ltd.

This paper describes the formulation and verification of a novel observer of wind parameters. The general idea behind the proposed approach is to consider the wind turbine rotor as an anemometer. In fact, the rotor responds to varying wind conditions; by properly interpreting this response, one can indirectly measure some desired wind characteristics, as for example yaw and shear, as described here. Measurements of wind conditions obtained this way are not affected by the usual disturbances of existing sensors, for example when installed in the nacelle or in the rotor wake. Furthermore, the approach provides rotor-equivalent quantities, and not the typical point information provided by wind vanes, anemometers or other similar sensors, whose information might be too local for large rotors. The proposed method is here formulated for the observation of wind direction and vertical shear. The new observer is demonstrated first in a comprehensive simulation study using high-fidelity aeroservoelastic models, and then experimentally using an aeroelastically-scaled wind tunnel model.

The present work describes a method for the structural optimization of wind turbine rotor blades for given prescribed aerodynamic shape. The proposed approach operates at various description levels producing cost-minimizing solutions that satisfy desired design constraints at the finest modeling level. At first, a “coarse”-level constrained design optimization is performed by using a 1D spatial geometrically exact beam model for aero-servo-elastic multibody analysis and load calculation, integrated with a 2D FEM cross sectional model for stress/strain analysis, and the evaluation of the 1D model fully-populated cross sectional stiffness matrices. Next, a “fine”-level 3D FEM model is used for the refinement of the coarse-level solution. Improved results obtained at the level of the 3D model are utilized at the following coarse-level iteration through a heuristic modification of the design constraints. In addition, a buckling analysis is performed at the fine description level, which in turn affects the nonstructural blade mass. The updated constraint bounds and mass make their effects felt at the next coarse-level constrained design optimization, thereby closing the loop between the coarse and fine description levels. The multilevel optimization procedure is implemented in a computer program and it is demonstrated on the design of a multi-MW horizontal axis wind turbine rotor blade.

This work is concerned with the quantification of uncertainties associated with wind turbines. A part form the understanding of the Effects of uncertainties per se, the efficient propagation of uncertainties is necessary for the implementation of robust design optimization methods, which is one of our future goals. Among all possible sources of uncertainties, here uncertainties related to the incoming wind and to the aerodynamic characteristics of the blades are propagated throughout a high-fidelity multibody aeroservoelastic tool. Different techniques, which could all be used for propagating uncertainties other than the ones considered here, are tested and compared. These include different formulations from the family of Non-Intrusive Polynomial Chaos Expansion, as well as Ordinary and Universal Kriging. By running a reduced subset of standard design load cases, a comparison among the various methods is drawn in terms of accuracy and computational efficiency with respect to a standard Monte Carlo approach. It is concluded that, for the uncertain- ties considered here, all approaches lead to a significantly higher performance compared to Monte Carlo, with Universal Kriging slightly standing out. It is also observed that the output parameters exhibit significant variations, and this highlights the importance of a comprehensive framework for the quantification and propagation of uncertainties in wind energy systems.

The wind field leaves its fingerprint on the rotor response. This fact can be exploited to use the rotor as a sensor: by looking at the rotor response, in the present case in terms of blade loads, one may infer the wind characteristics. This paper describes a wind state observer that estimates four wind parameters, namely the vertical and horizontal shears and the yaw and upflow misalignment angles, from out-of-plane and in-plane blade bending moments. The resulting observer provides on-rotor wind inflow characteristics that can be exploited for wind turbine and wind farm control. The proposed formulation is evaluated by extensive numerical simulations in turbulent and non-turbulent wind conditions using a high-fidelity aeroservoelastic model of a multi-MW wind turbine.

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.