Table of contents

On behalf of the European Academy of Wind Energy (EAWE¹) and WindEurope (formerly the European Wind Energy Association, EWEA, until April 2016), we have the pleasure of presenting the Scientific Proceedings of the WindEurope Summit 2016.

WindEurope Summit 2016 took place in Hamburg from 27-29 September 2016 and was one of the largest and most important meeting places of the wind energy industry, featuring 50 thematic sessions with more than 300 oral and 200 poster presentations. Known previously as EWEA's Annual Conference and as the European Wind Energy Conference before that, it has included a Science & Research component since 2007. The event is a well-known meeting place that allows engineers and scientists to present their latest findings to a mixed audience consisting of both researchers and industry experts. The industry involvement helps to focus the research, while the scientists often demonstrate relevant, novel ideas and solutions to problems of current industrial interest. This often leads to interesting comments and discussions that have an impact well beyond the conference boundaries.

In the course of continuously improving the conference experience, in 2015 the organisers decided to combine scientific and general content in all conference sessions, rather than reserving the presentation of the Science & Research contributions in a separate track as was done at previous editions. Sessions at EWEA Annual Event 2015 were therefore combinations of general and of scientific presentations, which allowed for a more flexible choice of topics. In particular small research topics could also be accommodated in the context of a wider session. This successful setup of the conference has been carried over to this year's event in Hamburg.

The improvements continue this year, with an agreement between WindEurope and IOP Publishing, which allows for the scientific contributions to be published in the Journal of Physics: Conference Series, an Open Access journal managed by the Institute of Physics (IOP). The European Academy of Wind Energy has for some time recommended such an arrangement, and we are very happy to see this happen. The most important advantage is that conference papers will be much more visible, being fully indexed and online available free of charge to anyone, while the authors retain their copyright under the permissive IOP Proceedings License. The IOP also provides long-term archival and access.

All papers published in this volume originate from scientific contributions presented in the oral sessions of WindEurope Summit 2016, and have been peer-reviewed in order to ensure strict, international research standards. Selected papers, suitably extended and rewritten, will additionally be invited and considered for the new Wind Energy Science journal of the EAWE.

The proceedings in this volume consist of 21 papers covering a wide spectrum of wind energy research. The topics include aerodynamic investigations and blade design, controller design for load mitigation, forecasting of power production, investigations related to wind turbine wakes, atmospheric stability and monitoring of turbines, as well as contributions concerned with floating support

structures, optimal strategies for bidding on the energy market and mathematical failure modeling for offshore wind turbines, among others.

As with previous events, the European Academy of Wind Energy is responsible for organizing the review process for scientific contributions, has contributed to develop the sessions, and provides scientific chairs for all sessions. All papers were peer-reviewed by a Scientific Committee, consisting of scientists from EAWE member institutes and their associates. We thank all authors for their willingness to take part in this procedure, including the strict deadlines imposed by the tight publication schedule, and the reviewers for their hard work alongside their daily business. WindEurope is responsible for the organisation and logistics of the conference, and we thank their highly professional staff and their associates for the excellent collaboration.

¹http://www.eawe.eu/

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.

Wind resource assessment is a key aspect of wind farm planning since it allows to estimate the long term electricity production. Moreover, wind speed time-series at high resolution are helpful to estimate the temporal changes of the electricity generation and indispensable to design stand-alone systems, which are affected by the mismatch of supply and demand. In this work, we present a new generalized statistical methodology to generate the spatial distribution of wind speed time-series, using Switzerland as a case study. This research is based upon a machine learning model and demonstrates that statistical wind resource assessment can successfully be used for estimating wind speed time-series. In fact, this method is able to obtain reliable wind speed estimates and propagate all the sources of uncertainty (from the measurements to the mapping process) in an efficient way, i.e. minimizing computational time and load. This allows not only an accurate estimation, but the creation of precise confidence intervals to map the stochasticity of the wind resource for a particular site. The validation shows that machine learning can minimize the bias of the wind speed hourly estimates. Moreover, for each mapped location this method delivers not only the mean wind speed, but also its confidence interval, which are crucial data for planners.

The annual energy production of an existing wind farm including thermal stratification is calculated with two different methods and compared to the average of three years of SCADA data. The first method is based on steady state computational fluid dynamics simulations and the assumption of Reynolds-similarity at hub height. The second method is a wake modelling calculation, where a new stratification transformation model was imposed on the Jensen an Ainslie wake models. The inflow states for both approaches were obtained from one year WRF simulation data of the site. Although all models underestimate the mean wind speed and wake effects, the results from the phenomenological wake transformation are compatible with high-fidelity simulation results.

Commercially available wind yield assessment models rely on superposition of wakes calculated for isolated single turbines. These methods of wake simulation fail to account for emergent flow physics that may affect the behaviour of multiple turbines and their wakes and therefore wind farm yield predictions. In this paper wake-wake interaction is modelled computationally (CFD) and physically (in a hydraulic flume) to investigate physical causes of discrepancies between analytical modelling and simulations or measurements. Three effects, currently neglected in commercial models, are identified as being of importance: 1) when turbines are directly aligned, the combined wake is shortened relative to the single turbine wake; 2) when wakes are adjacent, each will be lengthened due to reduced mixing; and 3) the pressure field of downstream turbines can move and modify wakes flowing close to them.

In the UK, there is an interest in the expected offshore wind resource given ambitious national plans to expand offshore capacity. There is also an increasing interest in alternative datasets to evaluate wind seasonal and inter-annual cycles which can be very useful in the initial stages of the design of wind farms in order to identify prospective areas where local measurements can then be applied to determine small-scale variations in the marine wind climate. In this paper we analyse both MERRA2 reanalysis data and measured offshore mast data to determine patterns in wind speed variation and how they change as a function of the distance from the coast. We also identify an empirical expression to estimate wind speed based on the distance from the coast. From the analysis, it was found that the variations of the seasonal cycles seem to be almost independent of the distance to the nearest shore and that they are an order of magnitude larger than the variations of the diurnal cycles. It was concluded that the diurnal variations decreased to less than a half for places located more than 100km from the nearest shore and that the data from the MERRA2 reanalysis grid points give an under-prediction of the average values of wind speed for both the diurnal and seasonal cycles. Finally, even though the two offshore masts were almost the same nearest distance from the coast and were geographically relatively close, they exhibited significantly different behaviour in terms of the strength of their diurnal and seasonal cycles which may be due to the distance from the coast for the prevailing wind direction being quite different for the two sites.

In spite of the efforts made at the time of installation of wind vanes or ultrasonic anemometers (Sonic), there is always a remaining uncertainty of several degrees in the absolute north of such sensors. In this research a method is presented to reduce the azimuthal orientation error of wind direction sensors by means of Doppler Lidar measurements. The method is based on the comparison between the conventional sensor and a distant long range lidar pointing to it in staring mode. By comparing their line-of-sight wind speeds any misalignment between both systems can be estimated more accurately. This method was applied in an measurement campaign in the offshore wind farm alpha ventus next to the meteorological mast FINO 1. The maximum alignment error of a Sonic was reduced to below ±1°. This accurate alignment has asserted, that no bias exists between Lidar and Sonic wind speed measurements.

An accurate prediction of wind power output is crucial for efficient coordination of cooperative energy production from different sources. Long-time ahead prediction (from 6 to 24 hours) of wind power for onshore parks can be achieved by using a coupled model that would bridge the mesoscale weather prediction data and computational fluid dynamics. When a forecast for shorter time horizon (less than one hour ahead) is anticipated, an accuracy of a predictive model that utilizes hourly weather data is decreasing. That is because the higher frequency fluctuations of the wind speed are lost when data is averaged over an hour. Since the wind speed can vary up to 50% in magnitude over a period of 5 minutes, the higher frequency variations of wind speed and direction have to be taken into account for an accurate short-term ahead energy production forecast. In this work a new model for wind power production forecast 5- to 30-minutes ahead is presented. The model is based on machine learning techniques and categorization approach and using the historical park production time series and hourly numerical weather forecast.

This study is focused on assessing the ability of two refined large wind farm models to describe the disturbance of the neutral atmospheric flow caused by large offshore wind farms. Sensitivity studies of internal boundary layer parameters are carried out. An optimum large wind farm correction is then proposed and combined with two different standard single wake models, the Park and EVM models. The large wind farm wake effect is evaluated and validated against measurements of two offshore wind farms at Horns Rev and Nysted and four standard wake models by computing velocity deficit and normalized power. All large wind farm models proposed were able to capture wake width to some degree and the decrease of power output moving through the wind farm. Despite some uncertainties, this very promising model combinations allows us to take into account the slowdown in large wind farms.

In the present study numerical investigations of a generic Multi-Megawatt slender bladed Horizontal-Axis Wind Turbine (HAWT) under yawed inflow conditions were conducted. A three-dimensional URANS flow solver based on structured overlapping meshes was used. The simulations were conducted at wind speeds of 7m/sec, 11 m/sec and 15 m/sec for different yaw angles ranging from +60° to -60°. It was concluded that, for below rated wind speeds, under small yaw angles (below ±15°) the magnitudes of the blade forces are slightly increased, while under high yaw angles (above ±15°) there is a significant decrease. Moreover, the load fluctuations, for the different yaw angles, have the same frequency but different amplitude and oscillation shape. It was concluded that at the above rated wind speed of 15 m/sec, the blade aerodynamic loads are significantly affected by the yaw inflow conditions and the magnitude values of the loads are decreased with increasing yaw angle. It can be concluded that the angle of attack and the tower interference are the utmost variables affecting the yawed turbines.

The accurate prediction of stress histories for the fatigue analysis is of utmost importance for the design process of wind turbine rotor blades. As detailed, transient, and geometrically non-linear three-dimensional finite element analyses are computationally weigh too expensive, it is commonly regarded sufficient to calculate the stresses with a geometrically linear analysis and superimpose different stress states in order to obtain the complete stress histories.

In order to quantify the error from geometrically linear simulations for the calculation of stress histories and to verify the practical applicability of the superposition principal in fatigue analyses, this paper studies the influence of geometric non-linearity in the example of a trailing edge bond line, as this subcomponent suffers from high strains in span-wise direction. The blade under consideration is that of the IWES IWT-7.5-164 reference wind turbine. From turbine simulations the highest edgewise loading scenario from the fatigue load cases is used as the reference. A 3D finite element model of the blade is created and the bond line fatigue assessment is performed according to the GL certification guidelines in its 2010 edition, and in comparison to the latest DNV GL standard from end of 2015.

The results show a significant difference between the geometrically linear and non-linear stress analyses when the bending moments are approximated via a corresponding external loading, especially in case of the 2010 GL certification guidelines. This finding emphasizes the demand to reconsider the application of the superposition principal in fatigue analyses of modern flexible rotor blades, where geometrical nonlinearities become significant. In addition, a new load application methodology is introduced that reduces the geometrically non-linear behaviour of the blade in the finite element analysis.

The present article investigates the potential of Active Trailing Edge Flaps (ATEF) in terms of increase in annual energy production (AEP) as well as reduction of fatigue loads. The basis for this study is the DTU 10 MW Reference Wind Turbine (RWT) simulated using the aeroelastic code HAWC2. In an industrial-oriented manner the baseline rotor is upscaled by 5% and the ATEFs are implemented in the outer 30% of the blades. The flap system is kept simple and robust with a single flap section and control with wind speed, rotor azimuth, root bending moments and angle of attack in flap's mid-section being the sensor inputs. The AEP is increased due to the upscaling but also further due to the flap system while the fatigue loads in components of interest (blade, tower, nacelle and main bearing) are reduced close to the level of the original turbine. The aim of this study is to demonstrate a simple and applicable method that can be a technology enabler for rotor upscaling and lowering cost of energy.

This study presents the numerical results of two passive strategies to reduce the support structure loads of a large offshore wind turbine. In the first approach, an omnidirectional tuned mass damper is designed and implemented in the tower top to alleviate the structural vibrations. In the second approach, a viscous fluid damper model which is diagonally attached to the tower at two points is developed. Aeroelastic simulations are performed for the offshore 10MW INNWIND.EU reference wind turbine mounted on a jacket structure. Lifetime damage equivalent loads are evaluated at the tower base and compared with those for the reference wind turbine. The results show that the integrated design can extend the lifetime of the support structure.

Model scale experiments of floating offshore wind turbines are important for both platform design for the industry as well as numerical model validation for the research community. An important consideration in the wave tank testing of offshore wind turbines are scaling effects, especially the tension between accurate scaling of both hydrodynamic and aerodynamic forces. The recent MARINTEK braceless semisubmersible wave tank experiment utilizes a novel aerodynamic force actuator to decouple the scaling of the aerodynamic forces. This actuator consists of an array of motors that pull on cables to provide aerodynamic forces that are calculated by a blade-element momentum code in real time as the experiment is conducted. This type of system has the advantage of supplying realistically scaled aerodynamic forces that include dynamic forces from platform motion, but does not provide the insights into the accuracy of the aerodynamic models that an actual model-scale rotor could provide. The modeling of this system presents an interesting challenge, as there are two ways to simulate the aerodynamics; either by using the turbulent wind fields as inputs to the aerodynamic model of the design code, or by surpassing the aerodynamic model and using the forces applied to the experimental turbine as direct inputs to the simulation. This paper investigates the best practices of modeling this type of novel aerodynamic actuator using a modified wind turbine simulation tool, and demonstrates that bypassing the dynamic aerodynamics solver of design codes can lead to erroneous results.

This paper discusses about the wake structure of wind turbine via the use of URANS and Quasi-DNS, focussing on the tip vortex breakdown. The moving overlapped structured grids CFD Solver based on a fourth-order reconstruction and an all-speed scheme, rFlow3D is used for capturing the characteristics of tip vortices. The results from the Model Experiments in Controlled Conditions project (MEXICO) was accordingly selected for executing wake simulations through the variation of tip speed ratio (TSR); in an operational wind turbine, TSR often changes in value. Therefore, it is important to assess the potential effects of TSR on wake characteristics. The results obtained by changing TSR show the variations of the position of wake breakdown and wake expansion. The correspondence between vortices and radial/rotational flow is also confirmed.

2D wind tunnel tests at high Reynolds numbers have been done within the EU FP7 AVATAR project (Advanced Aerodynamic Tools of lArge Rotors) on the DU00-W-212 airfoil and at two different test facilities: the DNW High Pressure Wind Tunnel in Gottingen (HDG) and the LM Wind Power in-house wind tunnel. Two conditions of Reynolds numbers have been performed in both tests: 3 and 6 million. The Mach number and turbulence intensity values are similar in both wind tunnels at the 3 million Reynolds number test, while they are significantly different at 6 million Reynolds number. The paper presents a comparison of the data obtained from the two wind tunnels, showing good repeatability at 3 million Reynolds number and differences at 6 million Reynolds number that are consistent with the different Mach number and turbulence intensity values.

The aim of this work is to investigate the validity of simulation codes based on the Blade Element Momentum (BEM) theory for three important design load conditions. This paper includes the cases of yawed inflow, rotor tower interaction for downwind turbines and the standstill case. Computational Fluid Dynamics (CFD) and experimental data (when available) are used for the evaluation of the obtained results. For the yawed inflow, the results indicate that significant deviations between BEM and experiments & CFD can be observed. This discrepancy is caused by unsteady phenomena such as the advancing & retreating blade effect and the skewed wake effect. In the case of the rotor and tower interaction of the downwind turbine, the results show that the BEM based code overpredicts the sectional forces in terms of the normal and tangential forces by 20%. In the case of standstill, the evaluation of the results based on tip deflections shows clear differences in the output of both numerical approaches. While the flapwise deflections show a reasonable agreement, the CFD-based coupled solver predicts much larger edgewise vibrations.

One of the biggest challenges to introduce Tension Leg Platform (TLP) technology into the Offshore Wind market are the Transport & Installation (T&I) stages, since most of TLPs are not self-stable as semisubmersible or SPAR platforms, and consequently requires additional means to perform these operations. This paper addresses this problem that has been overcome through the development of a Semi-submersible "Transport & Installation" Barge (SSB) for Iberdrola's TLPWIND^® floating support structure. The Semi-submersible Barge has been designed both through the use of numerical models and an extensive basin testing campaign carried out at the University of Strathclyde facilities. This paper also includes an estimation of the duration in time to carry out the installation process of a Floating Offshore Wind Farm, comprising 100x5MW TLPWIND^® units in different scenarios.

The present paper provides insight into the validation of computer models used for simulations of offshore wind turbines. The offshore turbines are affected by environmental conditions that must be logged during the measurement campaign of the prototype and used for the simulations during the validation process.

A simple generic methodology is presented to be used for the comparison of statistical data from the measurement campaign and the simulations. This allows a better analysis of the simulations and helps limit the apparition of outliers in the measurements.

An example of the use of the methodology is provided with the use of the data recorded for the AD5-116 5MW turbine at alpha ventus. For it, the turbine power, operational parameters and blade and tower loads are compared.

The ability to detect faults and predict loads on a wind turbine drivetrain's mechanical components cost-effectively is critical to making the cost of wind energy competitive. In order to investigate whether this is possible using the readily available power converter current signals, an existing permanent magnet synchronous generator based wind energy conversion system computer model was modified to include a grid-side converter (GSC) for an improved converter model and a gearbox. The GSC maintains a constant DC link voltage via vector control. The gearbox was modelled as a 3-mass model to allow faults to be included. Gusts and gearbox faults were introduced to investigate the ability of the machine side converter (MSC) current (I_q) to detect and quantify loads on the mechanical components. In this model, gearbox faults were not detectable in the I_q signal due to shaft stiffness and damping interaction. However, a model that predicts the load change on mechanical wind turbine components using I_q was developed and verified using synthetic and real wind data.

Modelling the failure behaviour of wind turbines is an essential part of offshore wind farm simulation software as it leads to optimized decision making when specifying the necessary resources for the operation and maintenance of wind farms. In order to optimize O&M strategies, a thorough understanding of a wind turbine's failure behaviour is vital and is therefore being developed at Fraunhofer IWES. Within this article, first the failure models of existing offshore O&M tools are presented to show the state of the art and strengths and weaknesses of the respective models are briefly discussed. Then a conceptual framework for modelling different failure mechanisms of wind turbines is being presented. This framework takes into account the different wind turbine subsystems and structures as well as the failure modes of a component by applying several influencing factors representing wear and break failure mechanisms. A failure function is being set up for the rotor blade as exemplary component and simulation results have been compared to a constant failure rate and to empirical wind turbine fleet data as a reference. The comparison and the breakdown of specific failure categories demonstrate the overall plausibility of the model.

Wind energy is one of the most important source of renewable energy characterized by a significant growth in the last decades and giving a more and more relevant contribution to the energy supply. One of the main disadvantages of a faster integration of wind energy into the energy mix is related to the visual impact of wind turbines on the landscape. In addition, the siting of new massive infrastructures has the potential to threaten a community's well-being if new projects are perceived being unfair. The public perception of the impact of wind turbines on the landscape is also crucial for their acceptance. The implementation of wind energy projects is hampered often because of a lack of planning or communication tools enabling a more transparent and efficient interaction between all stakeholders involved in the projects (i.e. developers, local communities and administrations, NGOs, etc.). Concerning the visual assessment of wind farms, a critical gap lies in effective visualization tools to improve the public perception of alternative wind turbines layouts. In this paper, we describe the advantages of a 3D dynamical and interactive visualization platform for an augmented reality to support wind energy planners in order to enhance the social acceptance of new wind energy projects.

The increasing penetration of wind generation in power systems to fulfil the ambitious European targets will make wind power producers to play an even more important role in the future power system. Wind power producers are being incentivized to participate in reserve markets to increase their revenue, since currently wind turbine/farm technologies allow them to provide ancillary services. Thus, wind power producers are to develop offering strategies for participation in both energy and reserve markets, accounting for market rules, while ensuring optimal revenue. We consider a proportional offering strategy to optimally decide upon participation in both markets by maximizing expected revenue from day-ahead decisions while accounting for estimated regulation costs for failing to provide the services. An evaluation of considering the same proportional splitting of energy and reserve in both day- ahead and balancing market is performed. A set of numerical examples illustrate the behavior of such strategy. An important conclusion is that the optimal split of the available wind power between energy and reserve strongly depends upon prices and penalties on both market trading floors.