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An experimental investigation on scaled wind turbine models in a wind tunnel with a microphone array is presented. Our study focuses on the localization and quantification of aerodynamic noise sources on rotating wind turbine blades with the aim of identifying the contributing factors that have an impact on the source spectra. Therefore, wind tunnel measurements were conducted for three different blade geometries (NACA 4412 shape, Clark‐Y shape, and sickle shape), five pitch angles between −2° and +8° and five wind velocities between 5 and 13 mŝ−1. For the localization of rotating sound sources with a microphone array, a rotating beamforming method based on the acoustic ray method is used. The Clean‐SC deconvolution method was used to improve the resolution of the acoustic sources, and integrated spectra were calculated for the individual blades. The sound sources were localized at the wind turbine blades and assigned to the leading edge and trailing edge subregions. The results show a high dependency on the sound source distribution and the source strength with regard to the observed one‐third octave bands, wind velocity, and blade geometry. Hence, the localization of rotating sound sources with a microphone array is a suitable method for the development of wind turbine blades that emit less noise.

Wind turbine blade leading edge erosion reduces the lift and increases the drag of the blade airfoils. This occurrence, in turn, reduces turbine power and energy yield. This study focuses on the aerodynamic analysis of large and sparse erosion cavities, observed in intermediate to advanced erosion stages, whose size and surface pattern do not lend themselves to experimental and numerical analysis by means of distributed roughness models alone. Making use of three‐dimensional Navier‐Stokes computational fluid dynamics enhanced by laminar‐to‐turbulent transition modeling, and geometrically resolving individual erosion cavities, the study validates this simulation‐based approach for predicting the aerodynamics and performance loss of blade sections featuring the aforementioned erosion cavities against available experimental data. It is found that the considered cavities can trigger transition, indicating the necessity of both resolving their geometry in the simulations and also modeling distributed surface roughness, of typically lower level, as this latter affects the properties of boundary layers and, if sufficiently high, may trigger transition over the entire spanwise length affected. The energy yield loss of a utility‐scale turbine due to the considered erosion pattern is found to be between 2.1% and 2.6% using measured and computed force data of the nominal and eroded outboard blade airfoil. A parametric analysis of the cavity geometry suggests that the geometry of the cavity edge has a much larger impact on aerodynamic performance than the cavity depth.

When the number of wind farms increases, the number of wind turbines placed in forested areas will also increase, this is because of the large forest areas in Sweden. When the wind turbines are placed in forested areas the fatigue loads increase drastically, so their maintenance costs increase making them less profitable. In this work clearings in forest are studied to be able to increase the electrical power generated by the wind turbines and decrease the fatigue loads to make them more profitable. The approach in this work is to use Computational Fluid Dynamics (CFD) with the Large Eddy Simulation (LES) turbulence model coupled to an aero elastic solver that solves the dynamic response of the wind turbine. Here Ryningsnas in Sweden is investigated with a homogeneous forest, the current clearing and an extended clearing. The electrical generator power was highest for the current clearing. But the fatigue loads were both higher and lower than the homogeneous forest depending on which part of the wind turbine that was investigated. The extended clearing nearly always had the lowest fatigue loads but unfortunately also the lowest electrical generator power. Further optimization of the clearings and the wind turbine locations in relation to them is needed to find the sweet spot where the fatigue loads are lower and the electrical generator power is higher.

Rain‐induced leading‐edge erosion (LEE) of wind turbine blades (WTBs) is associated with high repair and maintenance costs. The effects of LEE can be triggered in less than 1 to 2 years for some wind turbine sites, whereas it may take several years for others. In addition, the growth of erosion may also differ for different blades and turbines operating at the same site. Hence, LEE is a site‐ and turbine‐specific problem. In this paper, we propose a probabilistic long‐term framework for assessing site‐specific lifetime of a WTB coating system. Case studies are presented for 1.5 and 10 MW wind turbines, where geographic bubble charts for the leading‐edge lifetime and number of repairs expected over the blade's service life are established for 31 sites in the Netherlands. The proposed framework efficiently captures the effects of spatial and orographic features of the sites and wind turbine specifications on LEE calculations. For instance, the erosion is highest at the coastal sites and for sites located at higher altitudes. In addition, erosion is faster for turbines associated with higher tip speeds, and the effects are critical for such sites where the exceedance probability for rated wind conditions are high. The study will aid in the development of efficient operation and maintenance strategies for wind farms.

This paper presents a numerical study of the effects of blade roughness on wind turbine performance and annual energy production and how these effects may be partially mitigated through improved control. Three rotors are designed using the NACA4415, S801 and S810 airfoils, and blade element momentum theory is used to model wind turbine behaviour. The aerodynamic lift and drag data for the clean and roughened airfoils are taken from previous experimental work. These show that surface roughness leads to a decreased airfoil lift coefficient and an increased drag coefficient across an angle of attack range typical for large wind turbines, which will clearly lead to decreased turbine performance. Three separate control methods are considered for wind turbines with roughened blades operating at each of four candidate wind sites with different wind speed distributions. Results show that, compared to clean rotor blades, the roughened blades lead to a performance drop in the range of 2.9–8.6% for a torque based control strategy. A control-based performance recovery strategy, in which the controller gain coefficient is re-optimised, increased roughened rotor annual energy production by 0.1–1.0%.

A multi-rotor wind turbine (MRWT) is a concept that can reduce the size of the rotor blades compared to a single-rotor wind turbine (SRWT). Making a cost-optimized MRWT requires a detailed understanding of its stability properties. This paper aims to establish a physical and intuitive representation of whirling modes for three-bladed isotropic SRWT and MRWT. An aeroelastic simulation of a nonlinear SRWT model is presented to empathize the importance of whirling. The whirling concept is introduced by simplifying the complexity of the wind turbine rotor into two models. From the models, edgewise and flapwise whirling modes are analyzed. An analytical model of a two-rotor wind turbine is examined to present the edgewise whirling modes of MRWT. The flapwise whirling modes for MRWT are introduced by using results from edgewise whirling and findings from previous research. The MRWT whirling analysis shows whirling from multiple rotors creates reaction forces to the supporting structure when the rotors have the same speed. This results in whirling coupling modes at the same natural frequency. One is a rotor symmetric whirling mode where the rotors whirling are in phase and a rotor asymmetric mode where whirling of the rotors are out of phase. The whirling coupling effects are minimized in the case that the rotors have different speeds.

A new approach is proposed to do the velocity sampling for actuator‐line models (ALM). Unlike the conventional spatial averaging approaches, the velocity sampling is performed in a Lagrangian averaging way along flow paths with a time scale depending on the local flow patterns. The new approach is evaluated by large‐eddy simulations of a utility‐scale National Renewable Energy Laboratory (NREL)‐5MW wind turbine. The results show that the Lagrangian approach of velocity sampling is effective to reduce the spurious numerical oscillations in the modeling results. The good match with the benchmark data indicates that correct free upwind velocity can be obtained with a careful determination of the associated time scale. Also, a piecewise function is proposed to replace the Gaussian function for the force projection process.

Individual pitch control (IPC) is an effective and widely used strategy to mitigate blade loads in wind turbines. However, conventional IPC fails to cope with blade and actuator faults, and this situation may lead to an emergency shutdown and increased maintenance costs. In this paper, a fault‐tolerant individual pitch control (FTIPC) scheme is developed to accommodate these faults in floating offshore wind turbines (FOWTs), based on a Subspace Predictive Repetitive Control (SPRC) approach. To fulfill this goal, an online subspace identification paradigm is implemented to derive a linear approximation of the FOWT system dynamics. Then, a repetitive control law is formulated to attain load mitigation under operating conditions, both in healthy and faulty conditions. Since the excitation noise used for the online subspace identification may interfere with the nominal power generation of the wind turbine, a novel excitation technique is developed to restrict excitation at specific frequencies. Results show that significant load reductions are achieved by FTIPC, while effectively accommodating blade and actuator faults and while restricting the energy of the persistently exciting control action.

There is a need to clarify the coupling characteristics of terrain‐induced wind fields and wind turbine‐induced wake in wind farm micrositing. However, research investigating the effect of hill shape on this interaction is lacking. In addition, during the optimization of the layout of the turbines over topographies, the flow behind the turbines should be predicted in a fast way. After obtaining the wake flow of the turbine mounted on flat terrain and the flow over the topographies, there are mainly two superposition methods to predict the turbine wake flow over the topographies. One is to add the wind deficit following the location with a vertical distance of the hub height (D‐line). The other is to add the wind deficit following the streamline starting from the turbine center (B‐line). It remains unknown to what extent these two superposition methods can be adopted. Therefore, the effects of hill shape, wind turbine size, and turbine location are investigated. When the wind deficit, obtained from modeling the wake flow of the turbine mounted on flat terrain, is superposed following B‐line, the results are overall better than those when following D‐line.

Gearbox oil temperature is one of the important indicators for gearbox condition monitoring and faults early warning. Accurately predicting the gearbox oil temperature change trend can maintain the gearbox in advance and ensure the safety and reliability of the wind turbine gearbox. The purpose of this article is to analyze the supervisory control and data acquisition (SCADA) data in wind turbines. A method based on multi‐input improved ant lion optimization and support vector regression (M‐IALO‐SVR) proposed, which can accurately predict the gearbox oil temperature. The prediction method is compared with back propagation neural network (BPNN) and ALO‐SVR methods to verify the effectiveness of the M‐IALO‐SVR method. To further analyze the prediction results, the 95% confidence interval processing is performed on the residuals of the prediction model, and then the trends of the mean and standard deviation of the moving window residuals are calculated. Testing SCADA data from a wind farm in northeast China, the test results show that when the gearbox is operating normally, the predicted value of the gearbox oil temperature follows the measured value very well. When the gearbox operates abnormally, its temperature deviates from the normal range, and the statistical characteristics of the residuals also change. According to the trend of the residuals statistical characteristics, the abnormal state of the gearbox can be found in time.

This paper proposes a paradigm shift in the numerical simulation approach to predict rain erosion damage on wind turbine blades, given the blade geometry, its coating material, and the atmospheric conditions (wind and rain) expected at the installation site. Contrary to what has been done so far, numerical simulations (flow field and particle tracking) are used not to study a specific (wind and rain) operating condition but to build a large database of possible operating conditions of the blade section. A machine learning algorithm, trained on this database, defines a prediction module that gives the feature of the impact pattern over the 2‐D section, given the wind and rain flow. The advantage of this approach is that the prediction becomes much faster than using the standard simulations; thus, the study of a large set of variable operating conditions becomes possible. The module, coupled with an erosion model, is used to compute the erosion damage of the blade working on specific installation site. In this way, the variations of the flow conditions due to dynamic effects such as variable wind, wind turbulence, and turbine control can be also considered in the erosion computation. Here, we describe the method, the database creation, and the development of the prediction tool. Then, the method is applied to predict the erosion damage on a blade section of a reference wind turbine, after one year of operation in a rainy onshore site. Results are in good agreement with on field observations, showing the potential of the approach.

High‐order sliding mode control laws with gain adaptation algorithms are applied, in Region III, on a floating offshore wind turbine equipped by permanent magnet synchronous generator (PMSG). These adaptive control methods are especially efficient for systems with uncertainties and external perturbations and are well adapted to wind turbines systems. Such controllers can be implemented with very reduced knowledge of system model (only the relative degree is necessary) and strongly reduce the controller gains tuning effort. Simulations are made on Fatigue, Aerodynamics, Structures and Turbulence (FAST) software and compared with standard gain‐scheduled proportional–integral (PI) controllers.

Summary Disturbance accommodating control (DAC) has been developed in the last decades for wind turbines to control the rotor/generator speed and to reduce structural loads. The method allows accommodating unknown disturbance effects by using the combination of disturbance observers and disturbance rejection controllers. The actual main problem of DAC is to define suitable disturbance observer and controller gain matrices to achieve the desired overall performance including turbine speed regulation in combination with structural load mitigation. The disturbance rejection controller is often designed and tuned separately for individual applications and operating conditions. The closed‐loop system stability and uncertainties due to the use of the linearized reduced‐order model in controller synthesis procedure are not fully considered. This paper introduces a method to design DAC by optimizing the observer and controller parameters simultaneously to guarantee system performance respecting to structural loads mitigation, power regulation, and robustness. To eliminate the rotor speed control steady‐state error due to model uncertainties, partial integral action is included. Simulation results using NREL reference wind turbine models show that the proposed method successfully regulates the rotor speed without error despite the presence of the model uncertainties. Structural loads are also reduced using proposed method compared to DAC designed by Kronecker product method. The proposed approach is able to define a stable and robust DAC controller by solving a non‐smooth H∞ optimization problem with structure and stability constraints.

In the design of offshore wind farms the simulated dynamic response of the wind turbine structure includes loading from turbulent wind. The International Electrotechnical Commission (IEC) standard for wind turbine design recommends both the Mann spectral tensor model and the Kaimal spectral model combined with an exponential coherence formulation. These models give deviating wind loads. This study compares these two models to a large eddy simulations model and a model based on offshore wind measurements. The comparisons are performed for three situations, covering unstable, neutral and stable atmospheric conditions. The impact of the differences in the wind fields on the quasi-static response of a large bottom-fixed wind turbine is investigated. The findings are supported by an assessment of the impact of individual wind characteristics on the turbine responses. The wind model based on measurements causes high tower bottom and blade root flapwise bending moments due to a high wind load at very low frequencies. Low and negative horizontal coherence is obtained using the Mann spectral tensor model. This causes relatively large yaw moments as compared to the results using the other wind models. The largest differences in response are seen in the stable situation. We furthermore show that the quasi-static wind load has great impact on the total damage equivalent moments of the structure. From the results, we conclude that in the design of large offshore wind turbines one should carefully consider the structure of the turbulent wind. Further, longer simulations than recommended by the standards should be used to reduce uncertainty in estimated response. publishedVersion

Wind energy has been an attractive renewable energy source, and in recent decades, highly efficient horizontal axis wind turbines have been developed and successfully deployed. However, alternative designs for wind energy conversion have also been proposed. In previous studies, a novel swing sail design for wind energy harvesting was proposed and analyzed. This turbine harvests energy by pitching a sail, causing a mast to swing, which in turn drives a flywheel in a rectified rotational motion. The rotating flywheel is then used to drive an electric generator. The designed turbine was originally semiactive, while in the present study, the fully passive version of the turbine was proposed, and its performance was examined. The turbine consists of an oscillating sail attached to a reciprocally moving mast via a torsion spring, and the wind‐driven oscillatory motion is transformed into the rotational motion of a flywheel using a ratchet. Furthermore, a dry‐friction dynamometer was utilized as a simple way of extracting energy. The dynamical equations of the turbine were derived and were used to analyze the time response of the turbine. In the model, the correlations for the aerodynamic lift and drag forces for a flat plate were utilized. The results were validated using the computation fluid dynamics (CFD) simulation. The effects of various dynamical and geometric parameters of the turbine on the generated power and the turbine performance were also investigated.

Rain erosion on the leading edge of wind turbine blades is an intricate engineering challenge for the wind industry. Based on an energetic approach, this work proposes a methodology to characterise the erosion capacity of the raindrop impacts onto the leading edge blades. This methodology can be used with meteorological data from public institutions or from direct measurements at the wind turbine locations. The erosion characterisation is analysed using accumulative and per impact erosive variables, that is, total kinetic energy and kinetic energy per impact. To consider the frequency of impacts, two erosive variables are proposed, namely, total kinetic power and kinetic power per impact. These variables are calculated using the data from the Royal Netherlands Meteorological Institute (Koninklijk Nederlands Meteorologisch Instituut, KNMI) of the last 25 years jointly with the operation specifications of an actual wind turbine model (Suzlon S111). The main contribution to the erosive variables was found to be the wind speed because it controls the rotational velocity of the wind turbine. Also, the intensity of the rainfall and the frequency of meteorological data logging, that is, the temporal resolution of data, play a significant role.

Wind turbine wakes negatively impact downwind turbines in wind farms reducing their global efficiency. The reduction of wake-turbine interactions by actuating control on yaw angles and induction factors is an active area of research. In this study, the capability of spanwise-periodic wind turbine arrays with tilted rotors to reduce negative turbine-wakes interaction is investigated by means of large-eddy simulations. It is shown that by means of rotor tilt it is possible to replace turbine far wakes with high-speed streaks where the streamwise velocity exceeds the freestream velocity at hub height. Considering three aligned arrays of wind turbines, it is found that the global power extracted from the wind can be increased by tilting rotors of upwind turbine arrays similarly to what already known for the case of a single row of aligned turbines. It is further shown that global tilt-induced power gains can be significantly increased by operating the tilted turbines at higher induction rates. Power gains can be further increased by increasing the ratio of the rotor diameters and turbine spacing to the boundary layer thickness. All these findings are consistent with those of previous studies where streamwise streaks were artificially forced by means of arrays of wall-mounted roughness elements in order to control canonical boundary layers for drag-reduction purposes., Comment: revised version

Predicting the occurrence of strong and sudden variations in wind power, so‐called ramp events, has become one of the main challenges for the operation of power systems with large shares of wind power. In this paper, we investigate 14 ramp events of different magnitudes and minute‐scale durations observed by a dual‐Doppler radar system at the Westermost Rough offshore wind farm. The identified ramps are characterised using radar observations, turbine data and data from the Weather Research and Forecasting (WRF) model. A remote sensing‐based forecasting methodology that propagates wind speeds upstream of wake‐free turbines is extended here to the whole farm, by including corrections for wake effects. The methodology aims to probabilistically forecast the wind turbines' power in the form of density forecasts. The ability to predict ramp events of different magnitudes is evaluated and compared with probabilistic statistical and physical benchmarks. During the observed ramp events, the remote sensing‐based forecasting model strongly outperforms the benchmarks. We show here that remote sensing observations such as radar data can significantly enhance very short‐term forecasts of wind power.

The reduction of structural loads is becoming an important objective for the wind turbine control system due to the ever-increasing specifications/demands on wind turbine rated power and related growth of turbine dimensions. Among various control algorithms that have been researched in recent years, the individual pitch control has demonstrated its effectiveness in wind turbine load reduction. Since the individual pitch control, like other load reduction algorithms, requires higher levels of actuator activity, one must take actuator constraints into account when designing the controller. This paper presents a method for the inclusion of such constraints into a predictive wind turbine controller. It is shown that the direct inclusion of constraints would result in a control problem that is nonconvex and difficult to solve. Therefore, a modification of the constraints is proposed that ensures the convexity of the control problem. Simulation results show that the developed predictive control algorithm achieves individual pitch control objectives while satisfying all imposed constraints.

This paper investigates the characteristics of turbine wake which is the major factor determining the turbulence in wind farm and overall wind farm performance. Using the lattice Boltzmann method (LBM) and the large eddy simulation (LES) approach, dynamic simulations of detailed fluid flow and wake trajectory induced by the MEXICO wind turbine were carried out. Moreover, with the help of adaptive mesh refinement (AMR) method, the lattice units were allowed to be automatically refined to allocate more grid points on the solid walls and in the wake region. The simulation results were firstly compared with the experimental data for validation of the numerical model, as well as for investigation of the impact of sub‐grid scale (SGS) turbulence models. Within the present modeling framework, the characteristics of transient wake propagation, detailed flow field, and rotor‐tower interaction were investigated, and the effect of tip‐speed ratio was also analyzed. The present work indicates that, with a suitable SGS turbulence model, the LBM‐LES method is an effective way for dynamic simulations of wake structures induced by a wind turbine with a rotating rotor.

This paper investigates the dynamic modeling and robust control of an underactuated floating wind turbine for vibration suppression. The offshore wind turbine is equipped with a tuned mass damper on the floating platform. The Lagrange's equation is employed to establish the limited degree‐of‐freedom dynamic model. A novel disturbance observer‐based hierarchical sliding mode control system is developed for mitigating loads of the underactuated floating wind turbine. In the proposed control scheme, two prescribed performance nonlinear disturbance observers are developed to estimate and counteract unknown disturbances, where the load induced by wave is considered as a mismatched disturbance while the load caused by wind is treated as a matched disturbance. The hierarchical sliding mode controller regulates the states of such an underactuated nonlinear system. In particular, the first‐order sliding mode differentiator is used to avoid the tedious analytic computation in the sliding mode control design. The stability of the whole closed‐loop system is rigorously analyzed, and some sufficient conditions are derived to guarantee the convergence of the states for the considered system. Numerical simulations deployed on both the design model and the National Renewable Energy Laboratory 5‐MW wind turbine model are provided, which demonstrate great effectiveness and strong robustness of the proposed control scheme.\ud \ud

The distribution of the angles of attack over the span of a rotor blade, together with blade element theory, provides a useful framework to understand forces, performance and other fluid dynamic phenomena of axial‐flow rotors. However, the angle of attack is not straightforward to define for a three‐dimensional rotor, where the flow is perturbed by the blade circulation, shed vorticity and wake development. This paper evaluates six methods to extract the angles of attack from blade‐resolved CFD simulations of axial‐flow turbines. Simulations of two different rotors are presented: a low solidity rotor designed for wind and a higher solidity rotor designed for tidal stream energy conversion. Of the analysed methods, five were obtained from the literature and are tested in terms of their internal parameters. The remaining method is named the streamtube analysis method (SAM) and is presented as an improvement on analysis methods that azimuthally average the flow data on the rotor plane, referred to as azimuthal averaging techniques (AATs). The SAM method accounts for the expansion of the streamtubes in flow‐field velocity sampling and exhibits improved convergence on the internal parameters compared with AAT. The six methods are benchmarked in terms of the angles of attack, axial induction factors and the local lift and drag coefficients, identifying that most perform well and converge with each other despite the different underlying assumptions or modelling approaches. However, given the limitations and inherent dependency on internal parameters, the line averaging and SAM are suggested for general flow analysis application.

The fast‐growing offshore wind energy sector brings opportunities to provide a sustainable energy resource but also challenges in offshore wind turbine (OWT) operation and maintenance management. Existing operational simulation models assume deterministic input reliability and failure cost data, whereas OWT reliability and failure costs vary depending on several factors, and it is often not possible to specify them with certainty. This paper focuses on modelling reliability and failure cost uncertainties and their impacts on OWT operational and economic performance. First, we present a probabilistic method for modelling reliability data uncertainty with a quantitative parameter estimation from available reliability data resources. Then, failure cost uncertainty is modelled using fuzzy logic that relates a component's failure cost to its capital cost and downtime. A time‐sequential Monte Carlo simulation is presented to simulate operational sequences of OWT components. This operation profile is later fed into a fuzzy cost assessment and coupled with a wind power curve model to evaluate OWT availability, energy production, operational expenditures and levelised cost of energy. A case study with different sets of reliability data is presented, and the results show that impacts of uncertainty on OWT performance are magnified in databases with low components' reliability. In addition, both reliability and cost uncertainties can contribute to more than 10% of the cost of energy variation. This research can provide practitioners with methods to handle data uncertainties in reliability and operational simulation of OWTs and help them to quantify the variability and dependence of wind power performance on data uncertainties.

Horizontal axis wind turbines (HAWTs) experience three-dimensional rotational and unsteady aerodynamic phenomena at the rotor blades sections. These highly unsteady three-dimensional effects have a dramatic impact on the aerodynamic load distributions on the blades, in particular, when they occur at high angles of attack due to stall delay and dynamic stall. Unfortunately, there is no complete understanding of the flow physics yet at these unsteady 3D flow conditions, and hence, the existing published theoretical models are often incapable of modelling the impact on the turbine response realistically. The purpose of this paper is to provide an insight on the combined influence of the stall delay and dynamic stall on the blade load history of wind turbines in controlled and uncontrolled conditions. New dynamic stall vortex and nonlinear tangential force coefficient modules, which integrally take into account the three dimensional rotational effect, are also proposed in this paper. This module along with the unsteady influence of turbulent wind speed and tower shadow is implemented in a blade element momentum (BEM) model to estimate the aerodynamic loads on a rotating blade more accurately. This work presents an important step to help modelling the combined influence of the stall delay and dynamic stall on the load history of the rotating wind turbine blades which is vital to have lighter turbine blades and improved wind turbine design systems.

The mechanical torque input into the wind turbine drive train is a very useful measurement for tests performed on a test bench. To ensure the accuracy and the reliability, an accurate calibration of the torque measurement must be carried out and repeated within a certain period of time. However, owing to the high torque level and large structure size, such a calibration is both expensive and time consuming. To overcome this challenge, a new calibration method is proposed here. The method is based on the electrical power measurement, where a high level of accuracy is much easier to achieve. With the help of a special test process, a relationship between the torque-measuring signal and the electrical power can be established. The process comprises two tests with the drive train running in different operating modes. The calibration is possible by carrying out the same test process on several different torque levels. Detailed uncertainty analysis of the method is presented, whereby the uncertainty can be calculated by means of matrix operation and also numerically. As a demonstration, the implementation of the method on a test bench drive train that contains two 5-MW motors in tandem with the motors operating in a back-to-back configuration is also presented. Finally, some variations on the method and possible ways of achieving better accuracy are discussed. © 2020 The Authors. Wind Energy published by John Wiley & Sons Ltd

The hydrostatic wind turbine (HWT) is a type of wind turbine that uses hydrostatic transmission (HST) drivetrain to replace the traditional gearbox drivetrain. Without the fragile and expensive gearbox and power converters, HWT can potentially reduce the maintenance costs owing to the gearbox and power converter failures in wind power system, especially in offshore cases. We design an MFAC torque controller to regulate the pump torque of the HWT and compared to an H_inf torque controller. Then we design an MFAC pitch controller to stabilise the rotor speed of HWT and compared to a gain-scheduling proportional-integral (PI) controller and a gain-scheduling PI controller with anti-windup (PIAW). The results indicate that MFAC torque controller provides more effective tracking performance than the H_inf controller, and that MFAC pitch controller shows better rotor speed stabilisation performance in comparison with the gain-scheduling PI controller and PIAW.

This paper presents a detailed analysis of the rotor-tower interaction and the effects of the rotor's tilt angle and yaw misalignment on a large horizontal axis wind turbine. A high-fidelity aeroelastic model is employed, coupling computational fluid dynamics (CFD) and structural mechanics (CSM). The wind velocity stratification induced by the atmospheric boundary layer (ABL) is modeled. On the CSM side, the complex composite structure of each blade is accurately modeled using shell elements. The rotor-tower interaction is analyzed by comparing results of a rotor-only simulation and a full-machine simulation, observing a sudden drop in loads, deformations, and power production of each blade, when passing in front of the tower. Subsequently, a tilt angle is introduced on the rotor, and its effect on blade displacements, loads, and performance is studied, representing a novelty with respect to the available literature. The tilt angle leads to a different contribution of gravity to the blade deformations, sensibly affecting the stresses in the composite material. Lastly, a yaw misalignment is introduced with respect to the incoming wind, and the resulting changes in the blade solicitations are analyzed. In particular, a reduction of the blade axial displacement amplitude during each revolution is observed.

Offshore wind turbines suffer from asymmetrical loading (blades, tower, etc), leading to enhanced structural fatigue. As well as asymmetrical loading different faults (pitch system faults etc.) can occur simultaneously, causing degradation of load mitigation performance. Individual pitch control (IPC) can achieve rotor asymmetric loads mitigation, but this is accompanied by an enhancement of pitch movements leading to the increased possibility of pitch system faults, which exerts negative effects on the IPC performance. The combined effects of asymmetrical blade and tower bending together with pitch sensor faults are considered as a “co‐design” problem to minimize performance deterioration and enhance wind turbine sustainability. The essential concept is to attempt to account for all the “fault effects” in the rotor and tower systems, which can weaken the load reduction performance through IPC. Pitch sensor faults are compensated by the proposed fault‐tolerant control (FTC) strategy to attenuate the fault effects acting in the control system. The work thus constitutes a combination of IPC‐based load mitigation and FTC acting at the pitch system level. A linear quadratic regulator (LQR)‐based IPC strategy for simultaneous blade and tower loading mitigation is proposed in which the robust fault estimation is achieved using an unknown input observer (UIO), considering four different pitch sensor faults. The analysis of the combined UIO‐based FTC scheme with the LQR‐based IPC is shown to verify the robustness and effectiveness of these two systems acting together and separately.

Wind turbines are often plagued by premature component failures, with drivetrain bearings being particularly subjected to these failures. To identify failing components, vibration condition monitor ...