Table of contents

Preface

On behalf of the European Academy of Wind Energy (EAWE¹) and WindEurope (formerly the European Wind Energy Association EWEA), we hereby have the pleasure of presenting the Scientific Proceedings of the WindEurope Conference 2019.

WindEurope Conference & Exhibition 2019 took place in Bilbao on 2-4 April 2019 with the motto of "Delivering a clean economy for all European". The event is one of the largest and most important meeting places of the wind energy community. This year's conference has attracted more than 8000 participants and featured 30 thematic sessions, with contributions from more than 150 speakers and 200 poster presentations. As in previous years, the sessions mixed industrial content with scientific works, in order to increase the visibility of relevant scientific research and to facilitate an interchange of ideas between industry and academia.

As earlier, the scientific contributions of the WindEurope Conference 2019 are published in the Journal of Physics: Conference Series, an Open Access journal managed by the Institute of Physics (IOP). All papers in this volume originate from scientific contributions presented at WindEurope Conference 2019, in the form of posters or as talks, and have been peer-reviewed in order to ensure strict, international standards, coordinated by the Scientific Proceedings Editors and aided by the EAWE Wind Europe Scientific Committee and members from EAWE institutions.

All papers published in this volume of Journal of Physics: Conference Series have been peer reviewed through processes administered by the proceedings Editors. Reviews were conducted by expert referees to the professional and scientific standards expected of a proceedings journal published by IOP Publishing.

The aerodynamic performance of airfoils and blades designed for modern wind-turbine rotors, which have diameters of the order of hundred meters, must be examined at chord Reynolds numbers matching those of practical applications. In general, such high Reynolds numbers cannot be achieved in conventional wind tunnels. Moreover, knowledge on the boundary-layer transition location is essential to evaluate airfoil and blade performance at these flow conditions. This work presents an experimental methodology that can be applied at flow conditions reproducing those of real wind-turbine rotor blades and simultaneously provides aerodynamic coefficients and transition locations. The experimental methodology consists of: the Temperature-Sensitive Paint (TSP) technique for global, non-intrusive and reliable transition detection; conventional pressure measurements for the determination of the aerodynamic coefficients; and the High Pressure Wind Tunnel Göttingen (DNW-HDG) to run the experiments at Reynolds numbers matching those of real applications. The obtained results can be used to verify airfoil and blade performance and to validate numerical predictions. In the present work, the experimental methodology was applied to systematically investigate the aerodynamic performance of an airfoil designed for the mid-span sections of modern wind-turbine rotor blades. The examined chord Reynolds numbers were as high as 12 million and the angle-of-attack ranged from -14° to +20°. The presented methodology was here demonstrated to be mature for productive testing.

For a single wind turbine, the efficiency of extracting energy from the wind depends on the ability to align the wind turbine with the dominating wind direction. Considering average power production, yaw misalignment is relevant when the wind turbine operates with maximum power coefficient. On the other hand, the power production is less sensitive to yaw misalignment in high wind speeds, where the available energy in the wind field is higher than the maximum wind turbine capacity. In a wind farm, the interaction between nearby wind turbines alters the flow, and the power production is reduced. The present study investigates how yaw misalignment affects the power production in these wake situations compared to yaw misalignment effects for a wind turbine in the free-stream. Two generic cases are presented in this paper, offshore and forest, where the atmospheric conditions alter the morphology of the wake and, therefore, the power output of a yawed wind turbine operating in wake conditions. The results show that, for a conventional downstream spacing further than 3 rotor diameters, yaw misalignment results in larger power loss in wake situations than in free-stream. In wake situations, the presented results also show that the spatial distribution of the deficit influences the relative power loss when the wind turbine is operating in yawed conditions.

This paper presents improvements to the eddy viscosity wake model to significantly improve its fidelity without compromising its low computational cost. The eddy viscosity wind turbine wake model, proposed by Ainslie, has been a staple component of the wind yield toolchain for decades. Its low computational cost and excellent predictive power are likely to keep it as such for years to come. However, the standard implementations of the model rely heavily on several heuristics which, although well validated, may not scale well for large turbines or large windfarms.

In this paper, some non-physical heuristics are examined, and alternatives proposed which better reflect the underlying physics the model seeks to represent, which is shown to provide much better fidelity at no additional computational cost. The proposed model allows for better predictive capabilities of the performance of the next generation of offshore wind farms. The two key areas investigated are: first the impact of the ambient condition (TI, stability, ...) on wake development; and second a re-examination of the wake combination method, leading to the inclusion of three additional processes in the model. It is shown that modifications to these areas provide a more verifiable wake model than the baseline implementations, and allow identification of the remaining physical processes to be further incorporated into future versions.

An engineering wake model based on the Ainslie model is proposed. The eddy viscosity term associated with momentum diffusivity is modified to take into account the effects of atmospheric stability. The parameters used are typically available from high-quality on-site measurement campaigns and the effects of atmospheric stability are based on empirical models for the estimation of the Monin-Obukhov length. The dependence on physical quantities only is particularly advantageous for fast wake modelling, since no further parametrical tuning is needed for each specific case. The proposed wake model is initially compared to wind tunnel data and CFD simulations to test the chosen Obukhov lengths for specific flow conditions. Wind farm production data and concurrent meteorological data at one onshore site are then used to validate the model for specific on-site flow conditions, obtaining good results. Two offshore wind farms are also used to assess the model in a large-scale wind farm scenario: results look promising although some reservations are expressed on the effect of the wake superposition model. Models for the prediction of wake centreline deflection due to yaw are compared at different yaw angles using wind tunnels data and CFD simulations. Although the EPFL model showed some advantage, especially in non-neutral conditions, both models give satisfactory results. Furthermore, it was showed that the wake deflection caused by the rotating wake for non-yawed turbines can have a large impact on the predictions of the centreline deflection.

The Mesoscale Compressible Community (MC2) model [1], devoted for weather forecasting and used in the Wind Energy Simulation Toolkit (WEST) [2], performs well for simulations over flat, gentle and moderate terrain slopes but is subject to numerical instability and strong spurious flows in presence of steep topography. To remove its inherent computational mode and reduce the wind overestimation due to terrain-induced numerical noise, a new semi-implicit (N-SI) scheme [3] was implemented to discretize and linearize the non-hydrostatic Euler equations with respect the mean values of pressure and temperature instead of arbitrary reference state values, redefining as well the buoyancy to use it as the thermodynamic prognostic variable. Additionally, the climate-state classification of the statistical-dynamical downscaling (SDD) method [4] is upgraded by including the Brunt-Väisälä frequency that accounts for the atmospheric thermal stratification effect on wind flow over topography. The present study provides a real orographic flow validation of these numerical enhancements in MC2, assessing their individual and combined contribution for an improved initialization and calculation of the surface wind in presence of high-impact terrain. By statistically comparing the wind simulations with met-mast data, obtained within the Whitehorse area of the Canadian Rocky Mountains, it is confirmed that these numerical enhancements may reduce over 40 percent of the wind overestimation, thus, attaining more accurate results that ensure reliable wind resource assessments over complex terrain.

Computational modeling and lidar scanning data of the atmospheric flow over a double-ridge (Perdigão) were used to study a 24-hour period on 14–15 May 2017 with a maximum wind speed of more than 6 ms⁻¹. An influence of the stratification was observed throughout most of the time, in the form of a lee-wave triggered by the topography or flow separation further downstream of the ridge. The good agreement between the results obtained by lidar scanning and computational modeling increases our confidence in the use of either of these two techniques. See corresponding video at doi.org/10.11583/DTU.7863482.

Offshore wind energy is emerging as a large contributor to installed renewable energy capacity. In order to continue the momentum of its development, the offshore wind industry is looking to continually lower the levelized cost of electricity (LCOE). One area being explored in an effort to lower the LCOE of offshore wind generation is the optimization of the wind farm layout. Many of the offshore wind farm layout designs that exist today are structured in a rectilinear form where turbines are spaced evenly along columns and rows. This research explores the economic advantages of removing rectilinear constraints and optimizing the positions of the individual turbines within an offshore wind farm. At the core of achieving the research objective was the development of a model that is capable of simulating an existing offshore wind farm by converting representative wind farm data into an LCOE. The positions of the turbines within the wind farm can be modified using an optimization framework with the intent to minimize the LCOE. The model comprised of the Jensen Wake Model, a hybrid cable layout heuristic and a cost scaling model. The wind farm layout was optimized using a genetic algorithm. The cost estimation model and optimization framework were applied into two case studies to analyze the results of the wind farm layout optimization of two wind farms, Horns Rev and Borssele. In both case studies the optimized layouts provided higher AEP, shorter intra-array collection cable lengths and ultimately a lower LCOE than the baseline rectilinear layouts.

This contribution introduces a novel method to determine the aerodynamic damping for operating wind turbines. Previous research typically estimated the modal damping ratios in the fore-aft and side-side directions as two decoupled degrees of freedom. This can result in misleading results, as the two directions are closely and unconventionally coupled through the wind-rotor interaction. This study proposes the identification of a novel type of aerodynamic damping matrix. This matrix arises from the linearization of the aerodynamic force resultant obtained from blade element momentum theory (blade modes not included). This linearized force is then applied to a beam finite element model of the tower with a lumped mass representing the rotor-nacelle assembly. This decoupled strategy efficiently describes the physics of the system including the coupling between the fore-aft and side-side motions. The identification of the damping matrix is shown to work for simulated wind time data.

Both renewable energy supply and electricity demand are strongly influenced by meteorological conditions and their evolution over time in terms of climate variability and climate change. However, knowledge of power output and demand forecasting beyond a few days remains poor. Current methodologies assume that long-term resource availability is constant, ignoring the fact that future wind resources could be significantly different from the past wind energy conditions. Such uncertainties create risks that affect investment in wind energy projects at the operational stage where energy yields affect cash flow and the balance of the grid. Here we assess whether sub-seasonal to seasonal climate predictions (S2S) can skilfully predict wind speed in Europe. To illustrate S2S potential applications, two periods with an unusual climate behaviour affecting the energy market will be presented. We find that wind speed forecasted using S2S exhibits predictability some weeks and months in advance in important regions for the energy sector such as the North Sea. If S2S are incorporated into planning activities for energy traders, energy producers, plant operators, plant investors, they could help improve management climate variability related risks.

The local manufacturing of small wind turbines has become increasingly popular over the last decade, among international networks of renewable energy practitioners, for the implementation of rural electrification applications. Locally manufactured small wind turbines (LMSWTs), used most frequently in off-grid battery based renewable energy systems, typically utilize wooden blades and coreless axial flux permanent magnet generators due to their simple manufacturing process and local availability of materials. In this paper the effects of leading edge erosion on locally manufactured small wind turbine blades are researched experimentally, both in terms of power performance and acoustic noise emissions. From the research it is concluded that a yearly maintenance of the wooden blades of a LMSWT can lead to less acoustic noise emissions and better power performance, with the latter becoming more significant as the mean wind speed of the installation site increases. For practitioners, installers and manufacturers of LMSWTs, these findings can significantly guide manufacturing processes and techniques in terms of preventing leading edge erosion, while also advising preventative maintenance procedures on the field, in order to keep small wind electric systems quiet and productive.

Blade leading edge erosion has developed into a significant issue for the offshore wind industry. Protection solutions, including polymer coatings and tapes, are often applied to increase the blade lifetime. Experimental evaluation of protection systems is typically conducted in whirling arm rain erosion test rigs. Currently, there is no thoroughly validated method to relate the test results to real-world erosion performance. Furthermore, the design of rigs is not sufficiently limited to enable comparison of results between different rigs. Industry guideline, DNV-GL-RP-0171, provides a comparison method to address this issue. This paper describes the development of a droplet particle tracking Computational Fluid Dynamics methodology for rain erosion test rigs, which models the impact strike characteristics of a droplet, the number of impacts and the effect of rig aerodynamics. The methodology was applied to two rigs with different aerodynamics. Rain erosion tests were conducted in the rigs on identical coating and aluminium samples. The results were compared against predicted number of impacts from the DNV-GL guideline. Contradictory results were found, concluding that the guideline does not provide an accurate comparison between all test rigs, as it does not account for rigs where large aerodynamic effects cause droplet concentrations or droplet break-up.

Upscaling wind turbines has resulted in levelised cost of energy (LCoE) reductions. However, larger turbine diameters pose significant design challenges, often with conflicting requirements. For example, non-linear dynamics of aeroelastic tailored blades must be accurately predicted whilst, for the sake of efficient gradient-based design, it is also desirable to simplify the numerical definition of such blades—keeping design variables (DVs) to a minimum. This work presents and validates two features of the ATOM code (Aeroelastic Turbine Optimisation Methods), developed at the University of Bristol, that enable accurate and efficient modelling of large-scale wind turbine blades. Both an efficient parameterisation method and high-order beam elements illustrate the capacity for increasing the speed of gradient evaluations whilst accurately predicting blade dynamics—either by reducing DVs or simulation time. As a preliminary validation, aero-servo-elastic simulations from ATOM and an industry-standard software—DNV GL Bladed—are compared against field measurements gathered from an existing 7 MW turbine.

To speed up validation and prototyping of novel control and design concepts in wind energy, wind tunnel experiments should be used in synergy with numerical simulations and field experiments. The paper presents a wind tunnel setup consisting of a scaled wind turbine model MoWiTO and an active grid for generation of tailor-made and reproducible inflow conditions. To demonstrate that the presented experimental setup is suitable for fast validation of advanced control concepts, several control algorithms have been implemented and validated in various inflow conditions, such as turbulent and sheared inflows, and wind gusts. The tested control algorithms include the baseline collective pitch and torque control, individual pitch control for load reduction, and a control algorithms for compensation of static aerodynamic imbalances. All control algorithms resulted in an expected behaviour of the model wind turbine, thus emphasizing that the presented experimental setup can be used for fast and inexpensive testing of control concepts.

The platform-pitch motions of Floating Offshore Wind Turbines are highly influenced by the blade-pitch controller. However, in rough sea state conditions, the platform-pitch motions are dominated by the incident wave dynamics. Then, the properties of the designed controller to reduce the platform-pitch motions can be changed due to the sea state conditions. In this study, the drawbacks of a previously designed Aerodynamic Platform Stabiliser controller performance in rough sea state conditions are analysed. Furthermore, an additional blade load feedback control loop has been designed to improve the performance of the controller in rough sea state conditions. The preliminary results presented here show the potential of the blade load feedback control loop effectiveness to improve the Aerodynamic Platform Stabiliser control loop performance in rough sea state conditions. The time-domain simulations were carried out with the NREL 5-MW wind turbine mounted on the ITI Energy's barge in the fully coupled non-linear aero-hydro-elastic simulation tool FAST.

With the sustained drive towards higher power ratings for offshore wind turbines, the size of the turbine rotor and drivetrain components scale accordingly. Compact hydraulic transmissions are widely applied in high-load systems and form a business case for application in multi-megawatt offshore turbines. The Delft Offshore Turbine (DOT) is a hydraulic wind turbine concept replacing conventional drivetrain components with a single seawater pump. In the DOT concept, pressurized seawater is directed to a Pelton turbine-generator combination, located at a central electricity generation platform. An in-field test campaign is performed using a prototype DOT turbine with a retrofitted 500 kW hydraulic drivetrain, consisting of fixed-displacement components. As a result of this configuration, a feed-forward Pelton speed controller is derived and implemented for operating the Pelton turbine at maximum efficiency. However, the controller tuning is based on estimations of physical system properties, of which the resulting optimality is unknown. For verification of the implementation, the model-free, gradient-based and data-driven Extremum Seeking Control (ESC) optimization scheme is employed. Results show fast convergence of the algorithm and an average maximum power increase of 3 %. The algorithm is well suited for application to real-world systems, due to its simplicity and ease of tuning.

Leading edge erosion caused by raindrop impact is a key problem that needs to be overcome in the wind energy sector. Solutions up to date have not proved suitable, failing prematurely in their lifecycle at a cost to the wind industry. Failure mechanisms for rain erosion are not well understood, particularly the elastic and viscous polymer properties at the resulting high strain rates (10⁶ - 10⁹ Hz) of raindrop impacts. The effect of the inclusion of glycidyl polyhedral oligomeric silsesquioxane nanoparticles into a commercial polyurethane coating system was studied using nanoindentation and dynamic mechanical thermal analysis (DMTA). Results show that the inclusion of POSS improves damping, providing an alternative mechanism for energy dissipation without variation of T_g and minimal loss of stiffness. This presents a way of modifying current coating systems through the incorporation of POSS. Nanoindentation obtained previously unreported properties of the coating system (hardness, modulus and short-term recovery) and highlighted a correlation between loading rate and a reduction of short-term recovery. Nanoindentation was difficult for the modified leading-edge protection (LEP) samples as two phases were formed resulting in large standard deviations. DMTA results show modification of the LEP increases damping at lower temperature ranges introducing an additional mechanism of dissipating energy. Additionally, sweep data show an increase in elasticity at higher frequencies on the modified samples.

Ambitious worldwide renewable energy targets are pushing for wind energy to become a mainstream power source. Pitch bearings are some of the most vulnerable components of wind turbines. Inside bearings, damage in the inner raceways is significant due to the rolling contact fatigue (RCF) caused by balls. It is well known that methodologies for RCF recommended by international standards are not suitable for fatigue cases where fatigue mechanism encompass multiaxiality, as well as the loading being non-proportional, leading to unnecessary design over-costs. The objective of this work is to develop a new accurate methodology to predict the lifetime of the raceway for a pitch bearing under RCF failure mode and analyse the effect of the pitch control strategy. This methodology will help both, the manufacturer of the bearing and the manufacturer of the turbine, to design a concept focused on the overall performance of the machine.

Modern wind turbines have a driving torque in the range of up to 10 MNm. The measurement of such high torques poses challenges, as there is no standard measurement equipment for this torque level available today. During the EU funded EcoSwing project, the world's first superconducting low-cost and lightweight multi-megawatt wind turbine generator has been designed and tested on the DyNaLab nacelle test rig of Fraunhofer IWES. For this test campaign, a specifically designed torque measurement system has been developed to measure the torque directly at the flange of the device under test in order to evaluate the efficiency of the power train.

A numerical and experimental analysis of the trailing edge serrations effect on the aerodynamic performance of the NACA64₃418 airfoil is presented. 3D Reynolds-averaged Navier-Stokes simulations (RANS) of the flow over the airfoil with and without trailing edge serrations have been carried out with the Transitions SST turbulence model. From these calculations an empirical law for the prediction of the aerodynamic behaviour of the airfoil with serrations is derived. The law is validated through wind tunnel experiments. The NACA64₃418 airfoil has been tested with trailing edge serrations installed with different flap angles to analyze the effect of the parameter on the performance of the airfoil. Results show that the trailing edge serrations cause a significant increment in lift coefficient with low penalties in drag.

An increasing number of wind turbines are expected to be able to participate in the ancillary services that would normally be provided by conventional power plants, as the penetration of the wind energy in the electricity grid is increasing. Specifically, the turbines are needed to be able to control their active power based on the operator command. Constraining the possible output power, i.e. decreasing the power to less than the maximum or rated power, is therefore important for the wind turbine control system. When a turbine is operating under a de-rating strategy, the operating points of the turbine, i.e. the steady-state blade pitch and the tip speed ratio, change compared to the normal case and this needs to be considered within the controller design. Thus, this paper is to study the influence of the de-rating on the turbine controller gain-scheduling and its effect to power output and fatigue damage of the key turbine components. The results show that the wind turbine response is refined through tuning under the different down-regulation operation points, that translate into less fatigue damage of the key turbine components. For a typical multi-megawatt turbine, it is found that the main component lifetime damage can be reduced substantially. In numerical terms, there are up to 6.5% for the tower BM in fore-aft direction and 2.7% for the blade root flapwise direction, which can lead in a prolonged operational lifetime of the wind turbine.

Premature bearing outages at the planetary and high speed shaft stage of gearboxes are responsible for long down time periods of wind turbines [1, 2]. In order to prevent premature bearing outages against certain failure mechanisms, a new robustness test procedure for original-sized WT gearbox bearings is developed within the project WT-Bearing Center.NRW. The BRT serves to qualify bearings for a damage-free operation over the entire service life [3]. It combines a fatigue load spectrum and also additional special load events [4]. This paper describes the analysis of wind turbine loads during different operation conditions and the definition of special load events, based on this analysis. The aim of including special loads to the BRT is to consider the complex real bearing loads within the test procedure as precisely as possible.

Utilising additional variables, which are normally not measured, could bring significant improvements to condition monitoring and control of a wind turbine and farm. However, incorporating additional sensors to measure such variables could increase the cost significantly. As a solution, instead of equipping every turbine in the wind farm with an expensive sensor to measure such a variable, it is proposed that only one turbine be equipped with a sensor and the neighbouring turbines with an estimator that essentially replaces the sensor; that is, each estimator would subsequently estimate what the sensor would measure. Each estimator is constructed based on Neural Network, and as a result, the cost could be significantly reduced. Note that the only turbine equipped with a sensor is used to train the NN. This work presents the results of a preliminary study to examine the feasibility of the proposed approach.

The wind turbines in a wind farm must turn to face the wind to achieve maximum power production. Until now, each turbine achieves this individually: the nacelle-mounted wind vane measures the yaw misalignment, from which the turbine controller decides when to make a yaw correction by using a combination of heavy filtering and counters or dead-band hysteresis. In a wind farm context, each turbine could make use of information from its neighbours, and therefore, in principle, it should be possible to achieve better yaw control by using a centralised farm-level yaw control algorithm which uses input measurements from all the turbines, and then tells each turbine how to yaw. "Better" yaw control will always be a compromise between maximising energy production and minimising yaw actuator duty. Using a realistic dynamic simulation model of a wind farm which uses actual measured wind data as input, different possible yaw strategies are tested, and compared in terms of energy production and yaw actuator duty. The results indicate that centralised yaw control is likely to achieve a better compromise. There also is much current interest in the use of wake steering, where yaw setpoints are optimised to steer the wakes of some turbines away from others. Centralised yaw control may offer a better way to implement these setpoints, rather than sending yaw offsets to be acted on by the normal turbine yaw controllers with their own filtering, dead-band logic, etc.

An energy mix with a large contribution from wind power requires wind farm participation in frequency regulation. To do this effectively, it is necessary for wind turbines to curtail their power production by a certain amount, or 'delta', which serves as a reserve. Although it is relatively straightforward to achieve this in certain conditions, doing so consistently normally requires wind speed observers to estimate available power. A delta control method is proposed here, which does not rely on said observers and naturally extends popular torque and pitch control algorithms.

This study presents the results of the German research project HyStOH funded by German Federal Ministry of Economic Affairs and Energy (BMWi). The project consortium of German universities, wind turbine designers, wind farm developers and certification bodies design a novel semi-submersible steel structure with a single point mooring and self-aligning capabilities. The tower, which carries a 6 MW two-bladed downwind turbine, has an airfoil shaped cover, which supports the self-alignment of the full structure towards the main wind direction. The downwind operating wind turbine in combination with the lift force generating tower enables a passive yaw system acting against hydrodynamical impacts from waves and currents. Simulations in time domain applying the fully coupled aero-hydro-servo-elastic code Bladed_4.8 demonstrate the drift sensitivity and the self-aligning capabilities of the HyStOH design. The hydrodynamic coefficients for the simulations have been adjusted by calculations with an 1:1 model of the panel code panMARE, developed by Technical University of Hamburg-Harburg. The motivation of this analysis is to capture the complex coupled motions of an innovative FOWT by numerical simulation tools [1]. The analysis presents dynamic simulations of the HyStOH design operating in turbulent wind and irregular sea state with special focus on the yaw drift behaviour of the FOWT. The sensitivity study of yaw drift is based on numerous variations of wind-wave-current misalignments during normal operation. The results of the simulations demonstrate the weathervane capabilities of the airfoil shaped structures.

This paper shows an innovative approach concerning a new type of rotor main bearings for wind turbines. The conical sliding bearing with its flexible support structure is designed for the specific loads and operating conditions of wind turbines. It is robust, compact and offers superior serviceability since individually replaceable bearing pads obviate the disassembling of the whole drivetrain during repair/maintenance.

After a short description of the bearing design the results of functional testing under real conditions in full-scale on a 1 MW system test bench are presented. The achieved load-bearing properties, the mixed friction performance and the start-up behaviour will be discussed.

Offshore wind turbine generators (WTG) are commonly founded on single large diameter piles, named monopiles. These monopiles are subjected to significant lateral loads and thereby sizeable overturning bending moments mainly due to action of wind and wave forces; thus the critical geotechnical design situation for monopiles supporting WTGs is often related to lateral loading conditions. The Pile Soil Analysis (PISA) joint industry research project [1] has recently proposed a monopile design method which encompasses finite element (FE) calculations under a specific design framework. Soil reaction curves that are crucial for monopile design (i.e. lateral force and moment reactions along the shaft and at the base of the pile) are derived from FE calculations, subsequently calibrated and entered into a 1D model which is then used for design optimisation. This method is implemented within the PLAXIS MoDeTo (Monopile Design Tool) software. This paper presents results of a concept monopile design study under lateral monotonic loading with the use of the PLAXIS MoDeTo method.

Models of inflow-turbulence noise and turbulent-boundary-layer trailing-edge noise are compared to earlier measurements of a 200-kW vertical axis wind turbine so that conclusions regarding the origin of the aerodynamic noise can be drawn. The measurement campaigns, which aimed at establishing the noise emission value and locating the aerodynamic noise sources with a microphone array, are here both compared to further modified versions of the trailing-edge and inflow-turbulence models respectively. Unlike the case for horizontal axis wind turbine, inflow-turbulence noise is deemed as the prevailing noise mechanism. Reducing the self-induced turbulence could then be an effective way of lowering the noise levels for vertical axis wind turbines. Also, looking at the directivity of the inflow-turbulence noise model which indicate most noise in the cross-wind directions, a deviation from the standard downwind measurement position for measuring noise emission is suggested for the VAWT case.

This paper shows a multiphysical simulation approach for direct - drive generators, including electromagnetic force calculations and multibody simulation. The objective of this method is to determine the airgap width distribution of the generator during operation.

In this investigation different modelling depths of the electromechanical model are discussed. Especially the modelling of the electromagnetic force application and integration of flexible structural elements is discussed.

The presented paper introduces simulative method for the determination of load and pressure distribution in the main roller bearings in multi-MW wind turbines. This method has been developed using the example of a generic 6 MW research wind turbine (WT). The investigated drive train comprises of a main gearbox and is mounted with a four point suspension. The fixed rotor bearing is a spherical roller bearing and the floating rotor bearing is a cylindrical roller bearing. The load and pressure distribution is determined by finite element (FE) simulation under consideration of real hub loads and elastic surroundings. The hub loads have been calculated using Multibody Simulation. Three different rotor bearing models are introduced, which consider FE contact conditions, macro-geometry, stiffness and rolling element profile. The developed method is transferable to different rolling bearing types and can be used to improve bearing load and pressure distribution in multi-MW WTs under consideration of elastic surroundings (main shaft, frame and bearing housing).

The aim of the study is to analyse and characterize the primary vortices produced by single low profile vane-type vortex generators (VGs) of different heights positioned on a flat plate with a backward-facing ramp and adverse gradient pressure for an incident angle of 15º. The effect of the vortex generator is implemented by using a source term in the Navier-Stokes equations according to the so-called jBAY source term model. In order to carry out the parametric study of the primary vortex, Computational Fluid Dynamics (CFD) simulations have been performed for different VG heights using the Navier-Stokes equations at a Reynolds number of Reθ = 9100 based on the local boundary layer momentum thickness θ in open-source code OpenFOAM. As a preliminary result, the jBAY model reproduces relatively well the streamwise pressure coefficient distributions on the flat plate floor. Finally, the advantage of using this model over a fully mesh-resolved vortex generator model for certain cases must be remarked because a lower number of cells is needed in the model domain with a saving of computational time and resources.

A common challenge in the decision making process regarding operation and life extension of existing wind farms is the lack of accurate information about the actual dynamic states of the turbines in terms of its operation from inception. SCADA records normally contain limited number of channels, and are not necessarily kept for the entire operating period of the wind farm; design and site data may be outdated or inaccessible. Nevertheless, as long as a minimum amount of information is available, statistical analysis and augmentation with artificial intelligence based simulation can be used to supplement the information. In the present study, we delineate a combination of data analysis, physical modelling and machine learning, that produces a detailed assessment of the operating conditions experienced by a wind farm and establishes the corresponding power performance, loads and fatigue damage accumulation.

In Germany, more than one third of the installed wind energy capacity will leave the feed-in tariff funding between 2021 and 2025. Operators of affected turbines are therefore increasingly concerned with the design of profitable end-of-funding strategies. This requires feasibility analyses of both lifetime extension and repowering options and entails the subsequent challenge to determine the optimal lifetime extension and corresponding repowering timing. To support operators and other stakeholders dealing with wind turbines' end-of-life issues, this study presents a geographic information system that permits evaluating optimal end-of-funding strategies at different spatial scales reaching down to detailed analyses on individual turbine level. The decision support system processes topographic, wind, turbine, and finance data in an integrated system of resource simulations, spatial planning analyses and economic viability assessments. Case-study results show that a uniform end-of-funding strategy cannot be applied to all ageing turbines. Conducted sensitivity analyses rather indicate that the best strategy highly depends on various turbine-specific aspects, especially the location, type and maintenance costs as well as exogenous factors, including the developments of electricity spot market prices and tendered feed-in premiums. In light of latest trends regarding the exogenous factors, lifetime extension and repowering potentials increase. However, the results also indicate that dismantling, disposal and recycling of numerous ageing turbines will become a major challenge for the wind energy sector in the next decade.

Lifetime extension is receiving increasing attention because of ageing asset bases, the need for efficient use of capital budgets, and the optimistic lifetime assumptions used at the project design stage. Based on the industrial attention and the overall observable reduction in subsidies for new investments and repowering, especially in onshore wind energy, lifetime extension is expected to become essential in the future. This contribution presents a methodology for life extension assessment of individual onshore wind turbine towers, as the key structural components, using the joint aeroeastic-finite element analysis and taking account of wind directionality and stress magnification around the tower door. The results demonstrate that the spread in the wind rose provide the potential for tower lifetime extension, however, the stress concentration around the tower door and site-wide variations of wind characteristics have to be also taken into account. The outcomes of this paper indicates that the wind rose dispersion can also provide a lifetime extension potential in addition to a more benign weather and operational conditions.

In the coming years, an important number of offshore wind turbines will reach the end of their initially planned service life. In a wind turbine end-of-life scenario, owners can decide between extending the life of the asset, repowering the site or decommissioning. This decision-making process is affected by important sources of uncertainty, especially in offshore environments. The limited experience makes the decommissioning procedure challenging, as it is still largely unexplored. This work assesses the current state of knowledge about the challenges surrounding the decommissioning process of an offshore wind farm. The four main challenges encountered are identified and analysed in detail, namely the regulatory framework, the overall planning of the process, the logistics and vessels' availability, and the environmental impacts confronted. Ultimately, this paper aims at stimulating the dialogue among stakeholders and raising the awareness of adequately regulating and preparing the upcoming decommissioning of offshore wind farms in Europe.

Renewable energy has gained importance in recent years because of its cooperation in ecosystems, welfare and the circular economy. The new business model comprises new values for organizational sustainability. Moreover, renewable energy such as offshore energy is associated with innovation systems, knowledge and collaboration between organizations. The participation of the offshore energy sector has increased in the Portuguese renewable energy supply. It is known that renewable offshore energy suppliers will cover economic necessities. One of the strategies was to set up a new business model based on supplier organization networking for the construction of an offshore wind farm. This will contribute positively to lower fuel dependency, fewer imports, control of energy prices and market regulation in terms of supply/demand. The methodology used was an electronic questionnaire applied to emergent offshore energy cluster stakeholders. The SWOT analysis of the offshore energy sector contributes to an understanding of the offshore energy sector in Portugal and reveals the business model with which the stakeholders are faced. Moreover, it draws up a business strategy oriented towards competitiveness and cooperation with other energy sectors.

Wind Energy generation depends on the existence of wind, a meteorological phenomena intermittent by nature, with the consequence of generating uncertainty on the availability of wind energy in the future. The grid stability processes require continuous forecasting of wind energy generated. Forecasting wind energy can be performed either by using weather forecast data or by projecting (or regressing) the past time-series data observations into the future. This last method is the statistical or time series approach. Wind Time Series show non-linearity and non-stationarity properties, and these two properties increase the complexity of the forecasting task using statistical methodologies. In this paper we explore the use of deep learning techniques, which can represent non-linearity, to the wind speed prediction using the largest public wind dataset available, the Wind Toolkit from the National Renewable Laboratory of the US. Several deep network architectures like Multi Layer Perceptrons, Convolutional Networks or Recurrent Networks have been tested on the 126,692 wind-sites and with the results obtained valuable comparisons and conclusions have been obtained. The distribution of the wind sites across the North American Geography has allowed to include in the analysis relationships between terrain, wind forecast complexity and deep methods. With the developed testing workbench and with the availability of the Barcelona Supercomputing Center new architectures are being developed. This work concludes with the feasibility of deep learning architectures for the wind and energy forecasting.

Energy storage technologies are considered a promising solution for overcoming one of the most pertinent hurdles to high renewable energy penetration: the mismatch between energy supply and consumer demand. The intermittent nature of variable renewable energy technologies at high penetration rates leads to a loss of value for each unit of energy produced. Generation-side energy storage can allow wind turbines to alter their generation strategies and derive additional value through improved market participation. On-board storage leads to more efficient use of space and a potential for cost reductions. In the present work, a brief review of existing work on these aspects was undertaken, followed by a time-series analysis of an offshore 6 MW wind turbine coupled to an energy storage system. The performance of the wind+storage system was simulated using one year of data from the Egmond aan Zee offshore wind farm site. A statistical analysis was undertaken to estimate the required charge/discharge cycles and establish the required storage capacity under different operating conditions. A lithium-ion battery was then considered as the competing energy storage technology, and a cumulative damage model was applied based on the depth-of-discharge characteristics. Findings indicate that despite their competitive capital costs, battery technologies would have a limited lifetime resulting from high charging/discharging cycles. A more viable approach in the long-term could be to opt for technologies that are less dependent on charge/discharge cycles and which have a lifetime that can match that of the wind turbine itself.

This paper addresses the joint operation of wind plants with energy storage systems in multiple markets to increase the value of wind energy from an economic and technical point of view. The development of an optimized energy management allows scheduling the wind generation in energy markets, as well as contributing to the system stability through the joint participation in frequency ancillary services. The market optimization maximizes market revenues considering overall storage costs, while avoiding energy imbalances and market penalties. Moreover, wind power fluctuations, forecast errors and real-time reserve requirements are controlled by the energy storage system and managed afterward through the participation in continuous intraday market. Furthermore, model predictive control approach enables a high compliance of reserve requirements and a huge reduction of energy imbalances in real-time operation. Different energy storage capacities are selected in order to evaluate their cost-effectiveness enhancing the wind plant operation under the considered study case.

Access forecasting aims to predict the quality of transfer of maintenance technicians to/from vessels and the constituent offshore structures at the wind farm. This is highly dependent on sea state conditions as well as other environmental factors such as visibility. Typically, scheduling or dispatch decisions are made on the basis of deterministic forecasts of significant wave height, often coupled with service contracts where transfers are expected to be attempted below a threshold significant wave height. However, there is always uncertainty in a weather prediction which can be accounted for by probabilistic forecasts. The aim of this work is to explore visualisation ideas for transforming vessel specific access forecasts into an interpretable and intuitive decision-support tool. Three simple methods are proposed based on a score out of 10, classification of transfer conditions, and a threshold score. Methods for summarising access conditions for 2–5 days ahead are also developed. This new forecasting and visualisation capability has significant implications for marine coordinators and skippers who will be able to make better informed safety-critical decisions, with the potential for reductions in the cost-of-energy offshore.

A detailed understanding of wind turbine performance status classification can improve operations and maintenance in the wind energy industry. Due to different engineering properties of wind turbines, the standard supervised learning models used for classification do not generalize across data sets obtained from different wind sites. We propose two methods to deal with the transferability of the trained models: first, data normalization in the form of power curve alignment, and second, a robust method based on convolutional neural networks and feature-space extension. We demonstrate the success of our methods on real-world data sets with industrial applications.

Condition-based maintenance (CBM) is currently seen as the preferred approach to optimise operation and maintenance (O&M) strategies by the early detection and diagnosis of critical failures for wind turbines (WT). WT reliability is highly affected by failures of the mechanical components from the drive train, although doubly-fed induction generators (DFIG) also contribute to high failure rates and downtime. As DFIG is the dominant technology employed for variable speed WTs, dedicated failure detection and diagnosis techniques are required. Current signature analysis (CSA) has emerged as a powerful tool for this purpose, despite some limitations. In this work, the variability of CSA through time by means of spectra waterfall and the wavelet transform are investigated to overcome these limitations. The analysis of a faulty in-service WT DFIG presented in this work raises the usefulness of the suggested approaches to identify patterns related to failure development through time.

Prognostic and health monitoring addresses the issue of detecting faults and monitoring the current state of a wind turbine. Details about the fault's progression, and from there, the remaining useful lifetime, are key features in monitoring and industrial operation and maintenance planning. In order to avoid increase in operation and maintenance cost, as well as subjective human involvement, we present an online and automated monitoring framework for prediction of the remaining useful lifetime based on deep learning models. This framework includes training and re-training of predictive models with minimal oversight by the operators.

Further, we explore the dependency of various models' predictive abilities based on the input variables available, such as SCADA and secondary measurements. Especially deep learning approaches, such as neural networks, benefit greatly from the volume of data that can be extracted from modern-day turbines. This work utilizes upon the volume of data to present a case study on main bearing failures for 108 turbines. In the presented setting, predictions of the remaining useful lifetime of more than 90 days can be expected on average, outperforming the closest state-of-the-art estimate by almost a factor of two on average.

This work describes an autonomous condition monitoring framework to process and analyze data measured on wind turbine gearboxes. Industry 4.0 and the Industrial Internet of Things open the door for much more elaborate measurement and data analysis campaigns thanks to the reduction in cost of sensors and of processing power. This increase in data acquisition and handling potential is especially useful considering that most current state-of-the-art methods in signal processing often lead to large quantities of health indicators due to the multiple processing steps. Such large numbers of indicators become unfeasible to inspect manually when the data volume and the number of monitored turbines increases. Therefore, this paper illustrates a hybrid analysis approach that combines advanced signal processing methods with machine learning and anomaly detection. This approach is validated on an experimental wind turbine gearbox vibration data set.

Condition monitoring (CM) systems are installed in wind turbines (WTs) in order to avoid component downtime and reduce maintenance costs. Vibration monitoring is widely used for the WT gearbox, which is a component with a significant downtime. Given that the installed wind capacity grows, the volume of CM data increases, making manual interpretation of vibration signals challenging. Therefore, there is a need for an efficient and automated maintenance decision support system. The aim to this paper is to propose an automated framework for gearbox incipient failure diagnosis. The framework utilises vibration signals and performs health estimation and fault isolation based on signal processing and artificial intelligence (AI) techniques. The methodology is demonstrated through a case study of vibration data from operating WTs with known gearbox failures. The study can be used to optimise wind turbine maintenance actions.

The operations and maintenance of a wind farm constitutes a considerable source of expenditures therefore a holistic framework is herein presented aiming at optimising their management. The principles upon which the framework was developed belongs to the asset management discipline oriented towards the wind energy sector. Besides, it is understood from a cross functional perspective which also embraces information management and business aspects. The framework comprehends different modules conceived with the following purposes: (i) creation of an integral and comprehensive failure database, (ii) modelling of the wind turbines' reliability taking into account their operational context and (iii) defining of maintenance strategy within a life-cycle perspective. The practical aspects of the framework are demonstrated through a case study based on real-field data from the sector. Furthermore, the present research sets the foundation to consider all the proposed algorithms, processes and techniques within a Product-Service System approach, which entails important benefits for the OEMs an for the operators.