Alpha Ventus Research Articles

Implementation of a Simple Actuator Disk for Large-Eddy Simulation in the Weather Research and Forecasting Model (WRF-SADLES v1.2) for wind turbine wake simulation

Abstract. In this study, we present the development of a Simple Actuator Disk model for Large-Eddy Simulation (SADLES), implemented within the Weather Research and Forecasting (WRF) model, which is widely used in atmospheric research. The WRF-SADLES model supports both idealized studies and realistic applications through downscaling from realistic data, with a focus on resolutions of tens of meters. Through comparative analysis with the Parallelized Large-eddy Simulation Model (PALM) at resolutions of 10 and 30 m, we validate the effectiveness of WRF-SADLES in simulating the wake characteristics of a 5 MW wind turbine. Results indicate good agreement between WRF-SADLES at 30 m resolution and 10 m resolution and the PALM model. Additionally, we demonstrate a practical case study of WRF-SADLES by downscaling ERA5 reanalysis data using a nesting method to simulate turbine wakes at the Alpha Ventus wind farm in the south of the North Sea. The meso-to-micro downscaling simulation reveals that the wake effect simulated by WRF-SADLES at the FINO1 offshore meteorological mast station aligns well with the cup anemometer and lidar measurements. Furthermore, we investigate an event of farm-to-farm interaction, observing a 16 % reduction in ambient wind speed and a 38 % decrease in average turbine power at Alpha Ventus due to the presence of a wind farm to the southwest. WRF-SADLES offers a promising balance between computational efficiency and accuracy for wind turbine wake simulations, making it valuable for wind energy assessments and wind farm planning.

Analysis of Air Rescue for Offshore Wind Energy: A Retrospective Analysis of Structural and Process Quality for the Years 2014 to 2017

In order to achieve the emission targets required by the German Federal Environment Agency (Umweltbundesamt), the offshore wind industry has become 1 of the central building blocks of the so-called “energy turnaround.” After the first offshore wind farm (OWF), Alpha Ventus, started operation in 2010, the number of OWFs in the North Sea and Baltic Sea has grown steadily. Because of the ongoing growth of the industry, the number of workers on-site has more than quadrupled in recent years. Although the majority of OWFs are located in the exclusive economic zone up to 130 km from the mainland, the same legal provisions of the Occupational Health and Safety Act (Arbeitsschutzgesetz) apply here as on the mainland. This means that the operators of the wind farms are legally obligated to maintain a seamless rescue chain. Because of the distance to the mainland, rescue helicopters are the means of choice to ensure prompt emergency preclinical care and, if necessary, rapid evacuation. The company Northern HeliCopter GmbH, part of the DRF Luftrettung since 2019, offers as part of WINDEACare rescue helicopters stationed at 3 locations on the North Sea and Baltic Sea to its contract partners an emergency medical staffed air rescue service, which is operational 24 hours a day, 365 days a year. This article is the first to provide a detailed overview of a new field in prehospital emergency care on the high seas and, to this end, has recorded all offshore missions performed from 2014 through 2017 with evaluation of related medical and aeronautical data.

Probabilistic temporal extrapolation of fatigue damage of offshore wind turbine substructures based on strain measurements

Abstract. Substructures of offshore wind turbines are becoming older and beginning to reach their design lifetimes. Hence, lifetime extensions for offshore wind turbines are becoming not only an interesting research topic but also a relevant option for industry. To make well-founded decisions on possible lifetime extensions, precise fatigue damage predictions are required. In contrast to the design phase, fatigue damage predictions can be based not only on aeroelastic simulations but also on strain measurements. Nonetheless, strain-measurement-based fatigue damage assessments for lifetime extensions have been rarely conducted so far. Simulation-based approaches are much more common, although current standards explicitly recommend the use of measurement-based approaches as well. For measurement-based approaches, the main challenge is that strain data are limited. This means that measurements are only available for a limited period and only at some specific hotspot locations. Hence, spatial and temporal extrapolations are required. Available procedures are not yet standardised and in most cases not validated. This work focusses on extrapolations in time. Several methods for the extrapolation of fatigue damage are assessed. The methods are intended to extrapolate fatigue damage calculated for a limited time period using strain measurement data to a longer time period or another time period, where no such data are available. This could be, for example, a future period, a period prior to the installation of strain gauges or a period after some sensors have failed. The methods are validated using several years of strain measurement data from the German offshore wind farm Alpha Ventus. The performance and user-friendliness of the various methods are compared. It is shown that fatigue damage can be predicted accurately and reliably for periods where no strain data are available. Best results are achieved if wind speed correlations are taken into account by applying a binning approach and if a least some winter months of strain data are available.

On the effects of inter-farm interactions at the offshore wind farm Alpha Ventus

Abstract. The energy transition means that more and more wind farms are being built in favorable offshore sites like the North Sea. The wind farms affect each other as they interact with the boundary layer flow. This phenomenon is a topic of current research by the industry and academia as it can have significant technical and financial impacts. In the present study, we use data from the Alpha Ventus wind farm site to investigate the effects of inter-farm interactions. Alpha Ventus is the first offshore German wind farm located in the North Sea with a fully equipped measurement platform, FINO1, in the near vicinity. We look at the effects on the wind conditions measured at FINO1 before and after the beginning of operation of the neighboring farms. We show how measured quantities like turbulence intensity, wind speed distributions, and wind shear are evolving from the period when the park was operating alone in the area to the period when farms were built and operate in close proximity (1.4–15 km). Moreover, we show how the wind turbine's response in terms of loads and generator and pitch activity is affected using data from a turbine that is in the vicinity of the mast. The results show the wake effects in the directions influenced by the wind farms according to their distance with increased turbulence intensity, reduced wind speeds, and increased structural loading.

FAST.Farm load validation for single wake situations at alpha ventus

Abstract. The main objective of the presented work is the validation of the simulation tool FAST.Farm for the calculation of power and structural loads in single wake situations; the basis for the validation is the measurement database of the operating offshore wind farm alpha ventus. The approach is described in detail and covers the calibration of the aeroelastic turbine model, transfer of environmental conditions to simulations, and comparison between simulations and adequately filtered measurements. It is shown that FAST.Farm accurately predicts power and structural load distributions over wind direction with discrepancies of less than 10 % for most of the cases compared to the measurements. Additionally, the frequency response of the structure is investigated, and it is calculated by FAST.Farm in good agreement with the measurements. In general, the calculation of fatigue loads is improved with a wake-added turbulence model added to FAST.Farm in the course of this study.

Cover Picture: Steel Construction 2/2021

AbstractApproach to the alpha ventus offshore wind farm. The support structures are exposed to extreme fatigue loads. © Leibniz University Hannover, Institute for Steel Construction (s. article p. 74 ff.).

Evaluation of a roughness length parametrization accounting for wind–wave alignment in a coupled atmosphere–wave model

AbstractThe importance of wind energy as an alternative energy source has increased over the latest years with more focus on offshore winds. A good estimation of the offshore winds is thus of major importance for this industry. Up to now the effect of the wind–wave (mis)alignment has not yet been taken into account in coupled atmosphere–wave models to study the vertical wind profile and power production estimations of offshore wind farms. In this study the roughness length parametrization of Drennan et al. in 2003, and its extension addressing the wind–wave (mis)alignment proposed by Porchetta et al. in 2019, are investigated in the Coupled Ocean–Atmosphere–Wave–Sediment Transport (COAWST) model. This study shows that the yearly mean wind estimation at hub height (100 m) is improved by the roughness length parametrization of Porchetta et al. compared to Drennan. This is mainly due to the increased roughness of the former parametrization compare to the latter, even in aligned wind–wave conditions. This difference in roughness is caused by the dataset used to obtain the constants, deep‐water conditions versus mixed offshore conditions. Moreover, the roughness length parametrization of Porchetta et al. performs better in two of three alignment categories. Furthermore, similar model performances are obtained if we exclude the wind directions from the wind shadow zone of the measurement mast or the wind directions from the recently built Alpha Ventus wind farm, which is in close vicinity of the measurement mast. Investigating different wind conditions shows that the new roughness length parametrization of Porchetta et al. performs best for both offshore and onshore winds. Additionally, we show that the coupled model estimations of the vertical wind are only slightly affected by significant wave height estimations. Similar model performances for different accuracies of significant wave height estimations are presented. One exception is the perpendicular alignment category where the new roughness length of Porchetta et al. outperforms the roughness length of Drennan when investigating the wind estimations related to significant wave heights with a higher accuracy. The roughness length parametrization of Porchetta et al. reduced the power production overestimation of the coupled model from 5.7 to 2.8%. We also show that the standalone atmospheric model including the roughness length of Charnock in 1955 has a degraded performance compared to the coupled model including the roughness length parametrization of Porchetta et al. for yearly average wind profiles.

On the pile tension capacity of scoured tripod foundation supporting offshore wind turbines

The application of tripod foundations in offshore wind turbines means that the piles are subjected to obvious uplift loads, while a process of soil erosion around offshore foundations caused by waves and currents, i.e. scour, can result in a reduced foundation capacity in vertical direction. However, currently few efforts have been made to evaluate the scour behavior around the tripod foundation, and the literature available on the investigation of scour effect on the tension capacity of tripod pile is also scanty. This study is to present a simplified method for the analysis of pile tension capacity of scour-affected tripod foundation in sand, which can account properly for various dimensions of the local scour hole formed around the tripod piles as well as that of the local scour hole under the main column of the tripod foundation. Based on a field scour measurement at a tripod foundation in the Alpha Ventus test site, a series of parametric analyses are performed by using the present method on a tripod pile under a wide range of scour-hole dimensions of the tripod foundation. The effects of depth, width, slope angle of the scour hole around both the pile and the main column of the tripod foundation as well as that of pile slenderness on the tension capacity of the tripod pile are discussed.

Validation of Numerical Models of the Offshore Wind Turbine From the Alpha Ventus Wind Farm Against Full-Scale Measurements Within OC5 Phase III

Abstract The main objective of the Offshore Code Comparison Collaboration Continuation, with Correlation (OC5) project is validation of aero-hydro-servo-elastic simulation tools for offshore wind turbines (OWTs) through comparison of simulated results to the response data of physical systems. Phase III of the OC5 project validates OWT models against the measurements recorded on a Senvion 5M wind turbine supported by the OWEC Quattropod from the alpha ventus offshore wind farm. The following operating conditions of the wind turbine were chosen for the validation: (1) idling below the cut-in wind speed, (2) rotor-nacelle assembly (RNA) rotation maneuver below the cut-in wind speed, (3) power production below and above the rated wind speed, and (4) shutdown. A number of validation load cases were defined based on these operating conditions. The following measurements were used for validation: (1) strains and accelerations recorded on the support structure and (2) pitch, yaw, and azimuth angles, generator speed, and electrical power recorded from the RNA. Strains were not directly available from the majority of the OWT simulation tools; therefore, strains were calculated based on out-of-plane bending moments, axial forces, and cross-sectional properties of the structural members. The simulation results and measurements were compared in terms of time series, discrete Fourier transforms, power spectral densities, and probability density functions of strains and accelerometers. A good match was achieved between the measurements and models setup by OC5 Phase III participants.

Impact of atmospheric stability on X-band and C-band synthetic aperture radar imagery of offshore windpark wakes

C-band and X-band Synthetic Aperture Radar (SAR) data acquired by the Sentinel-1 and TerraSAR-X satellites are used to study atmospheric wakes behind offshore wind parks in the German Bight. A particular focus is on the impact of atmospheric stability on wake parameters like the wake length. Stability parameters are estimated from measurements taken at the FINO-1 observation platform. Based on a data set covering different seasons and concentrating on the first German offshore wind park Alpha Ventus (AV), it is shown that in this area stable atmospheric conditions favour longer wakes. This is first demonstrated for situations, where the wake behind AV was unperturbed by other neighbor wind parks. In this case, wakes of more than 30 km length are observed. In a second step, the more complicated situation with wake superposition from different neighboring wind parks is analysed. It is shown that in this case, the merged wakes can extend to more than 70 km downstream.The analysis is challenged by two factors. First of all, the FINO-1 platform is within the wind farm wakes for a certain range of wind directions. This means stability estimates for the upstream conditions are not straightforward to obtain in these conditions. The second complication is associated with an apparent increase in the radar cross section downstream of wind parks observed on many SAR scenes, typically within the first 10 km downstream the wind park. A semi-empirical model is proposed to explain this effect by an increased downward momentum flux associated with increased turbulence generated by the wind park. Applying numerical inversion methods, a couple of typical downstream wind speed profiles are reproduced with this model based on SAR derived estimates of the friction velocity.

Influence of atmospheric stability on the load spectra of wind turbines at alpha ventus

Dependence between fatigue loads on a wind turbine located in an offshore wind farm and atmospheric stability was analysed by using measurement data of alpha ventus. An investigation of the fatigue load spectra of the wind turbines was performed enabling a detailed evaluation besides the damage equivalent load. Therefore, load spectra of a turbine in free-stream and a turbine experiencing wake inflow conditions were compared. Atmospheric stability was taken into account by means of the Bulk Richardson number. It was shown, how atmospheric stability affects load spectra of tower base and blade root bending moment. Hereby, it was found that few events of high fatigue loading increase the damage equivalent load (DEL) substantially, especially in unstable conditions.

Large eddy simulation with realistic geophysical inflow of Alpha Ventus wind farm: a comparison with LIDAR field experiments

In the current paper we perform a high Reynolds number large eddy simulation (LES) study of the offshore Alpha Ventus wind farm in North Sea. Actuator line modeling has been used for the rotating turbines. The wind farm is driven by a neutral atmospheric boundary layer coupled with realistically varying mean wind flux and direction from cup and vane anemometer data. We carry out comparison of LES and LIDAR based experimental results obtained from low-elevation angle Plan Position Indicator (PPI) scans of the velocity structures between two turbines. The results indicate towards a more fundamental understanding on the capability of LES inflow methodology in capturing realistic large scale structures that are observed in wind farm flows.

Normal Behaviour Models for Wind Turbine Vibrations: Comparison of Neural Networks and a Stochastic Approach

To monitor wind turbine vibrations, normal behaviour models are built to predict tower top accelerations and drive-train vibrations. Signal deviations from model prediction are labelled as anomalies and are further investigated. In this paper we assess a stochastic approach to reconstruct the 1 Hz tower top acceleration signal, which was measured in a wind turbine located at the wind farm Alpha Ventus in the German North Sea. We compare the resulting data reconstruction with that of a model based on a neural network, which has been previously reported as a data-mining algorithm suitable for reconstructing this signal. Our results present evidence that the stochastic approach outperforms the neural network in the high frequency domain (1 Hz). Although neural network retrieves accurate step-forward predictions, with low mean square errors, the stochastic approach predictions better preserve the statistics and the frequency components of the original signal, retaining high accuracy levels. The implementation of our stochastic approach is available as open source code and can easily be adapted for other situations involving stochastic data reconstruction. Based on our findings we argue that such an approach could be implemented in signal reconstruction for monitoring purposes or for abnormal behaviour detection.

Current and turbulence measurements at the FINO1 offshore wind energy site: analysis using 5-beam ADCPs

We report concurrent measurements of ocean currents and turbulence at two sites in the North Sea, one site at upwind of the FINO1 platform and the other 200-m downwind of the Alpha Ventus wind farm. At each site, mean currents, Reynolds stresses, turbulence intensity and production of turbulent kinetic energy are obtained from two bottom-mounted 5-beam Nortek Signature1000s, high-frequency Doppler current profiler, at a water depth of approximately 30 m. Measurements from the two sites are compared to statistically identify the effects of wind farm and waves on ocean current variability and the turbulent structure in the water column. Profiles of Reynolds stresses are found to be sensible to both environmental forcing and the wind farm wake-induced distortions in both boundary layers near the surface and the seabed. Production of turbulent kinetic energy and turbulence intensity exhibit approximately similar, but less pronounced, patterns in the presence of farm wake effects.

Offshore Wind Turbine Wake characteristics using Scanning Doppler Lidar

Within an offshore wind park, wind flow characteristics are quite complex and govern both the energy production and the structural wind turbine response. An experimental study focussed on assessing the spatial variability of winds near the German offshore wind energy platform FINO1 was conducted using multiple remote sensing devices. This study focuses on measuring the wind turbine wake characteristics, such as velocity deficit, the extent (length and width) of the wake and wake meandering under various atmospheric conditions using the data collected from a single scanning Doppler Lidar for several months in 2016. A new algorithm based on using a Gaussian model to measure the downwind wake characteristics is developed. The wind turbine downwind wake deficits compared well to previous models at far-wake regions, while at near-wake regions the models deviated due to different instruments & methodologies used in measuring the wake characteristics. It was also observed that the length of the Alpha Ventus wind turbine wake varied from 3 to 15 times the Rotor Diameter (RD), and the maximum velocity deficit varied from 55% to 75% of the free-stream wind speed, depending on mean wind speed and atmospheric stability. Detailed analysis of the Alpha Ventus wind turbine wake characteristics is presented.

Dynamic Data Filtering of Long-Range Doppler LiDAR Wind Speed Measurements

Doppler LiDARs have become flexible and versatile remote sensing devices for wind energy applications. The possibility to measure radial wind speed components contemporaneously at multiple distances is an advantage with respect to meteorological masts. However, these measurements must be filtered due to the measurement geometry, hard targets and atmospheric conditions. To ensure a maximum data availability while producing low measurement errors, we introduce a dynamic data filter approach that conditionally decouples the dependency of data availability with increasing range. The new filter approach is based on the assumption of self-similarity, that has not been used so far for LiDAR data filtering. We tested the accuracy of the dynamic data filter approach together with other commonly used filter approaches, from research and industry applications. This has been done with data from a long-range pulsed LiDAR installed at the offshore wind farm ‘alpha ventus’. There, an ultrasonic anemometer located approximately 2.8 km from the LiDAR was used as reference. The analysis of around 1.5 weeks of data shows, that the error of mean radial velocity can be minimised for wake and free stream conditions.

Modelling of Wind Turbine Loads nearby a Wind Farm

Each wind turbine experiences a variety of loads during its lifetime, especially inside a wind farm due to the wake effect between the turbines. This paper describes a possibility to observe a load spectrum while considering wake effects in a wind farm by through the turbulence intensity. The turbulence intensity is distributed along the wind rose of Alpha Ventus. For each turbulence intensity, a Weibull characteristic is calculated. The resulting wind fields are used to determine the loads through a multibody simulation of an imaginary wind turbine located at FINO-1, representing a closely placed wind turbine at the outer edge of a wind farm. These loads are analyzed and summed up. As expected, the change of the turbulence intensity due to the wake effect has an impact on the internal loading of a wind turbine inside a wind farm. Based on the assumed loading conditions, the maximum loads increased by a factor of almost 2.5.

A Methodology for the Reconstruction of 2D Horizontal Wind Fields of Wind Turbine Wakes Based on Dual-Doppler Lidar Measurements

Dual-Doppler lidar is a powerful remote sensing technique that can accurately measure horizontal wind speeds and enable the reconstruction of two-dimensional wind fields based on measurements from two separate lidars. Previous research has provided a framework of dual-Doppler algorithms for processing both radar and lidar measurements, but their application to wake measurements has not been addressed in detail yet. The objective of this paper is to reconstruct two-dimensional wind fields of wind turbine wakes and assess the performance of dual-Doppler lidar scanning strategies, using the newly developed Multiple-Lidar Wind Field Evaluation Algorithm (MuLiWEA). This processes non-synchronous dual-Doppler lidar measurements and solves the horizontal wind field with a set of linear equations, also considering the mass continuity equation. MuLiWEA was applied on simulated measurements of a simulated wind turbine wake, with two typical dual-Doppler lidar measurement scenarios. The results showed inaccuracies caused by the inhomogeneous spatial distribution of the measurements in all directions, related to the ground-based scanning of a wind field at wind turbine hub height. Additionally, MuLiWEA was applied on a real dual-Doppler lidar measurement scenario in the German offshore wind farm “alpha ventus”. It was concluded that the performance of both simulated and real lidar measurement scenarios in combination with MuLiWEA is promising. Although the accuracy of the reconstructed wind fields is compromised by the practical limitations of an offshore dual-Doppler lidar measurement setup, the performance shows sufficient accuracy to serve as a basis for 10 min average steady wake model validation.

Recommendations for load validation of an offshore wind turbine with the use of statistical data: experience from alpha ventus

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.

Load extrapolations based on measurements from an offshore wind turbine at alpha ventus

Statistical extrapolations of loads can be used to estimate the extreme loads that are supposed to occur on average once in a given return period. Load extrapolations of extreme loads recorded for a period of three years at different measurement positions of an offshore wind turbine at the alpha ventus offshore test field have been performed. The difficulties that arise when using measured instead of simulated extreme loads in order to determine 50-year return loads will be discussed in detail. The main challenge are outliers in the databases that have a significant influence on the extrapolated extreme loads. Results of the short- and longterm extreme load extrapolations, comprising different methods for the extreme load extraction, the choice of the statistical distribution function as well as the fitting method are presented. Generally, load extrapolation with measurement data is possible, but care should be taken in terms of the selection of the database and the choice of the distribution function and fitting method.