scholarly journals The Role of Atmospheric Stability and Turbulence in Offshore Wind-Farm Wakes in the German Bight

2021 ◽  
Author(s):  
Andreas Platis ◽  
Marie Hundhausen ◽  
Astrid Lampert ◽  
Stefan Emeis ◽  
Jens Bange
Keyword(s):  
German Bight ◽  
Wind Farm ◽  
Atmospheric Stability ◽  
Wind Farms ◽  
Offshore Wind ◽  
Lapse Rate ◽  
Offshore Wind Farm ◽  
Near Surface ◽  
Stable Conditions ◽  
The Stability

AbstractAirborne meteorological in situ measurements as well as stationary measurements at the offshore masts FINO1 and FINO3 in the German Bight are evaluated in order to examine the hypothesis that the wake dissipation downstream of large offshore wind farms depends on atmospheric stability. A long-term study of the mast data for the years 2016 and 2017 demonstrates a clear dependence of stability on the wind direction. Stable conditions are predominantly expected during southerly winds coming from the land. The analysis of various stability and turbulence criteria shows that the lapse rate is the most robust parameter for stability classification in the German Bight, but further implies that stability depends on the measurement height. A near-surface (0 to 30 m), predominantly convective, layer is present and more stable conditions are found aloft (55 to 95 m). Combing the stability data with the airborne measurements of the offshore wind-farm wakes reveals the trend of a correlation between longer wake lengths and an increase in the initial wind-speed deficit downwind of a wind farm with stronger thermal stability. However, the stability correlation criteria with the wake length downstream of the four investigated wind farms, Godewind, Amrumbank West, Meerwind Süd/Ost, and Nordsee Ost, contain large variance. It is assumed that the observed scattering is due to the influence of the wind-farm architecture and temperature inversions around hub height. These, however, are crucial for the classification of stability and illustrate the complexity of a clear stability metric.

scholarly journals An analysis of offshore wind farm SCADA measurements to identify key parameters influencing the magnitude of wake effects

2017 ◽  
Vol 2 (2) ◽  
pp. 477-490 ◽  
Author(s):  
Niko Mittelmeier ◽  
Julian Allin ◽  
Tomas Blodau ◽  
Davide Trabucchi ◽  
Gerald Steinfeld ◽  
...  
Keyword(s):  
Wind Turbines ◽  
Wind Farm ◽  
Average Power ◽  
Atmospheric Stability ◽  
Wind Farms ◽  
Offshore Wind ◽  
Offshore Wind Farm ◽  
Offshore Wind Farms ◽  
Partial Load ◽  
Wake Effects

Abstract. For offshore wind farms, wake effects are among the largest sources of losses in energy production. At the same time, wake modelling is still associated with very high uncertainties. Therefore current research focusses on improving wake model predictions. It is known that atmospheric conditions, especially atmospheric stability, crucially influence the magnitude of those wake effects. The classification of atmospheric stability is usually based on measurements from met masts, buoys or lidar (light detection and ranging). In offshore conditions these measurements are expensive and scarce. However, every wind farm permanently produces SCADA (supervisory control and data acquisition) measurements. The objective of this study is to establish a classification for the magnitude of wake effects based on SCADA data. This delivers a basis to fit engineering wake models better to the ambient conditions in an offshore wind farm. The method is established with data from two offshore wind farms which each have a met mast nearby. A correlation is established between the stability classification from the met mast and signals within the SCADA data from the wind farm. The significance of these new signals on power production is demonstrated with data from two wind farms with met mast and long-range lidar measurements. Additionally, the method is validated with data from another wind farm without a met mast. The proposed signal consists of a good correlation between the standard deviation of active power divided by the average power of wind turbines in free flow with the ambient turbulence intensity (TI) when the wind turbines were operating in partial load. It allows us to distinguish between conditions with different magnitudes of wake effects. The proposed signal is very sensitive to increased turbulence induced by neighbouring turbines and wind farms, even at a distance of more than 38 rotor diameters.

scholarly journals A cross-sectional survey of physical strains among offshore wind farm workers in the German exclusive economic zone

BMJ Open ◽  
2018 ◽  
Vol 8 (3) ◽  
pp. e020157 ◽  
Author(s):  
Marcial Velasco Garrido ◽  
Janika Mette ◽  
Stefanie Mache ◽  
Volker Harth ◽  
Alexandra M Preisser
Keyword(s):  
Wind Farm ◽  
Wind Farms ◽  
Exclusive Economic Zone ◽  
Offshore Wind ◽  
Upper Body ◽  
Offshore Wind Farm ◽  
Cross Sectional Survey ◽  
Cross Sectional ◽  
Heavy Loads ◽  
Under Construction

ObjectivesTo assess the physical strains of employees in the German offshore wind industry, according to job type and phase of the wind farm (under construction or operation).DesignWeb-based cross-sectional survey.SettingOffshore wind farm companies operating within the German exclusive economic zone.ParticipantsMale workers with regular offshore commitments and at least 28 days spent offshore in the past year (n=268).Outcome measuresPhysical strains (eg, climbing, noise, working overhead, with twisted upper body or in confined spaces, vibration, heavy lifting, humidity, odours).ResultsThe most frequently mentioned physical strain was ’climbing’ with 63.8% of the respondents reporting to be always or frequently confronted with climbing and ascending stairs during offshore work. Work as a technician was associated with a greater exposition to noise, vibrations, humidity, cold, heat, chemical substances, lifting/carrying heavy loads, transport of equipment, working in non-ergonomic positions and in cramped spaces, as well as climbing.Indeed, statistical analyses showed that, after adjusting for phase of the wind farm, age, nationality, offshore experience, work schedule and type of shift, compared with non-technicians, working as a technician was associated with more frequently lifting/carrying of heavy loads (OR 2.58, 95% CI 1.58 to 4.23), transport of equipment (OR 2.06 95% CI 1.27 to 3.33), working with a twisted upper body (OR 2.85 95% CI 1.74 to 4.69), working overhead (OR 2.77 95% CI 1.67 to 4.58) and climbing (OR 2.30 95% CI 1.40 to 3.77). Working in wind farms under construction was strongly associated with increased and decreased exposure to humidity (OR 2.32 95% CI 1.38 to 3.92) and poor air quality (OR 0.58 95% CI 0.35 to 0.95), respectively.ConclusionsWorkers on offshore wind farms constitute a heterogeneous group, including a wide variety of occupations. The degree of exposure to detrimental physical strains varies depending on the type of job. Technicians are more exposed to ergonomic challenges than other offshore workers.

Computation of Drift Forces for Dynamic Positioning Within the Very Early Design Stage of Offshore Wind Farm Installation Vessels

2014 ◽  
Author(s):  
Philip H. Augener ◽  
Stefan Krüger
Keyword(s):  
Wind Farm ◽  
Wind Farms ◽  
Offshore Wind ◽  
Irregular Waves ◽  
Design Stage ◽  
Dynamic Positioning ◽  
Offshore Wind Farm ◽  
Early Design ◽  
Early Design Stage ◽  
Drift Forces

The German government has decided upon the changeover from fossil and nuclear based electrical power generation to renewable energies. Following from this offshore wind farms are erected in the exclusive economic zones of Germany. For the transportation and installation as well as the maintenance of the wind turbine generators very specialized vessels are needed. The capability of dynamic positioning even in very harsh weather conditions is one of the major design tasks for these vessels. For this reason it is important to know the external loads on the ships during station keeping already in the very early design stage. This paper focuses on the computation of wave drift forces in regular and irregular waves as well as in natural seaway. For validation the results of the introduced calculation procedure are compared to measured drift force data from sea-keeping tests of an Offshore Wind Farm Transport and Installation Vessel.

Optimization of Floating Offshore Wind Energy Systems Using an Extended Pattern Search Method

2016 ◽  
Author(s):  
Caitlin Forinash ◽  
Bryony DuPont
Keyword(s):  
Wind Farm ◽  
Wind Farms ◽  
Power Production ◽  
Pattern Search ◽  
Offshore Wind ◽  
Offshore Wind Farm ◽  
Power Development ◽  
Offshore Wind Farms ◽  
Onshore Wind ◽  
Extended Pattern Search

An Extended Pattern Search (EPS) approach is developed for offshore floating wind farm layout optimization while considering challenges such as high cost and harsh ocean environments. This multi-level optimization method minimizes the costs of installation and operations and maintenance, and maximizes power development in a unidirectional wind case by selecting the size and position of turbines. The EPS combines a deterministic pattern search algorithm with three stochastic extensions to avoid local optima. The EPS has been successfully applied to onshore wind farm optimization and enables the inclusion of advanced modeling as new technologies for floating offshore wind farms emerge. Three advanced models are incorporated into this work: (1) a cost model developed specifically for this work, (2) a power development model that selects hub height and rotor radius to optimize power production, and (3) a wake propagation and interaction model that determines aerodynamic effects. Preliminary results indicate the differences between proposed optimal offshore wind farm layouts and those developed by similar methods for onshore wind farms. The objective of this work is to maximize profit; given similar parameters, offshore wind farms are suggested to have approximately 24% more turbines than onshore farms of the same area. EPS layouts are also compared to those of an Adapted GA; 100% efficiency is found for layouts containing twice as many turbines as the layout presented by the Adapted GA. Best practices are derived that can be employed by offshore wind farm developers to improve the layout of platforms, and may contribute to reducing barriers to implementation, enabling developers and policy makers to have a clearer understanding of the resulting cost and power production of computationally optimized farms; however, the unidirectional wind case used in this work limits the representation of optimized layouts at real wind sites. Since there are currently no multi-turbine floating offshore wind farm projects operational in the United States, it is anticipated that this work will be used by developers when planning array layouts for future offshore floating wind farms.

scholarly journals FLOWSTAR-Energy: a high resolution wind farm wake model

2016 ◽  
Author(s):  
Amy Stidworthy ◽  
David Carruthers
Keyword(s):  
Boundary Layer ◽  
Wind Turbine ◽  
Complex Terrain ◽  
Wind Farm ◽  
Wind Farms ◽  
Measured Data ◽  
Offshore Wind ◽  
Offshore Wind Farm ◽  
Wake Model ◽  
Turbine Wake

Abstract. A new model, FLOWSTAR-Energy, has been developed for the practical calculation of wind farm energy production. It includes a semi-analytic model for airflow over complex surfaces (FLOWSTAR) and a wind turbine wake model that simulates wake-wake interaction by exploiting some similarities between the decay of a wind turbine wake and the dispersion of plume of passive gas emitted from an elevated source. Additional turbulence due to the wind shear at the wake edge is included and the assumption is made that wind turbines are only affected by wakes from upstream wind turbines. The model takes account of the structure of the atmospheric boundary layer, which means that the effect of atmospheric stability is included. A marine boundary layer scheme is also included to enable offshore as well as onshore sites to be modelled. FLOWSTAR-Energy has been used to model three different wind farms and the predicted energy output compared with measured data. Maps of wind speed and turbulence have also been calculated for two of the wind farms. The Tjaæreborg wind farm is an onshore site consisting of a single 2 MW wind turbine, the NoordZee offshore wind farm consists of 36 V90 VESTAS 3 MW turbines and the Nysted offshore wind farm consists of 72 Bonus 2.3 MW turbines. The NoordZee and Nysted measurement datasets include stability distribution data, which was included in the modelling. Of the two offshore wind farm datasets, the Noordzee dataset focuses on a single 5-degree wind direction sector and therefore only represents a limited number of measurements (1,284); whereas the Nysted dataset captures data for seven 5-degree wind direction sectors and represents a larger number of measurements (84,363). The best agreement between modelled and measured data was obtained with the Nysted dataset, with high correlation (0.98 or above) and low normalised mean square error (0.007 or below) for all three flow cases. The results from Tjæreborg show that the model replicates the Gaussian shape of the wake deficit two turbine diameters downstream of the turbine, but the lack of stability information in this dataset makes it difficult to draw conclusions about model performance. One of the key strengths of FLOWSTAR-Energy is its ability to model the effects of complex terrain on the airflow. However, although the airflow model has been previously compared extensively with flow data, it has so far not been used in detail to predict energy yields from wind farms in complex terrain. This will be the subject of a further validation study for FLOWSTAR-Energy.

scholarly journals Three-Dimensional Structure of Wind Turbine Wakes as Measured by Scanning Lidar

2017 ◽  
Author(s):  
Nicola Bodini ◽  
Dino Zardi ◽  
Julie K. Lundquist
Keyword(s):  
Wind Farm ◽  
Atmospheric Stability ◽  
Three Dimensional ◽  
Leading Edge ◽  
Wind Farms ◽  
Layout Optimization ◽  
Dimensional Structure ◽  
Strong Wind ◽  
Three Dimensional Structure ◽  
Stable Conditions

Abstract. The slower wind speeds and increased turbulence that are characteristic of turbine wakes have considerable consequences on large wind farms: turbines located downwind generate less power and experience increased turbulent loads. The structures of wakes and their downwind impacts are sensitive to wind speed and atmospheric variability. Wake characterization can provide important insights for turbine layout optimization in view of decreasing the cost of wind energy. The CWEX-13 field campaign, which took place between June and September 2013 in a wind farm in Iowa, was designed to explore the interaction of multiple wakes in a range of atmospheric stability conditions. Based on lidar wind measurements, we extend, present, and apply a quantitative algorithm to assess wake parameters such as the velocity deficits, the size of the wake boundaries, and the location of the wake centerlines. We focus on wakes from a row of four turbines at the leading edge of the wind farm to explore variations between wakes from the edge of the row (outer wakes) and those from turbines in the center of the row (inner wakes). Using multiple horizontal scans at different elevations, a three-dimensional structure of wakes from the row of turbines can be created. Wakes erode very quickly during unstable conditions, and can in fact be detected primarily in stable conditions in the conditions measured here. During stable conditions, important differences emerge between the wakes of inner turbines and the wakes of outer turbines. Further, the strong wind veer associated with stable conditions results in a stretching of the wake structures, and this stretching manifests differently for inner and outer wakes. These insights can be incorporated into low-order wake models for wind farm layout optimization or for wind power forecasting.

scholarly journals Current Source Converter Based Offshore Wind Farm: Configuration and Control

2021 ◽  
Author(s):  
Miteshkumar Nandlal Popat
Keyword(s):  
Wind Farm ◽  
Current Source ◽  
Wind Farms ◽  
Offshore Wind ◽  
Grid Integration ◽  
Offshore Wind Farm ◽  
Offshore Wind Farms ◽  
Control Scheme ◽  
Current Source Converter ◽  
Grid Side

Recently, offshore wind farms have emerged as the most promising sector in the global renewable energy industry. The main reasons for the rapid development of offshore wind farms includes much better wind resources and smaller environmental impact (e.g., audible noise and visual effect). However, the current state of the offshore wind power presents economic challenges significantly greater than onshore. In this thesis, a novel interconnecting method for permanent magnet synchronous generator (PMSG)-based offshore wind farm is proposed, where cascaded pulse-width modulated (PWM) current-source converters (CSCs) are employed on both generator- and grid-side. With the converters in cascade to achieve high operating voltages, the proposed method eliminates the need for bulky and very costly offshore converter substation which is usually employed in voltage source converter (VSC) high voltage DC (HVDC)-based counterparts. Related research in terms of control schemes and grid integration are carried out to adapt the proposed cascaded CSC-based offshore wind farm configuration. The large distance between generator- and grid-side CSC in the proposed wind farm configuration addresses significant challenges for the system control. In order to overcome the problem, a novel decoupled control scheme is developed. The active and reactive power control on the grid-side converters are achieved without any exchange of information from the generator-side controller. Therefore, the long distance communication links between the generator- and grid-side converters are eliminated and both controllers are completely decoupled. At the same time, the maximum power tracking control is achieved for the generator-side converters that enable full utilization of the wind energy. Considering inconsistent wind speed at each turbine, a coordinated control scheme is proposed for the cascaded CSC-based offshore wind farm. In proposed control strategy, the wind farm supervisory control (WFSC) is developed to generate the optimized dc-link current control. This enables all the turbines to independently track their own MPPT even with inconsistent wind speed at each turbine. Grid integration issues, especially the fault ride-through (FRT) capability for the cascaded CSC-based offshore wind farm are addressed. Challenges in implementing existing FRT methods to the proposed offshore wind farm are identified. Based on this, a new FRT strategy using inherent short circuit operating capability of the CSC is developed. Moreover, the mitigation strategy is developed to ensure the continuous operation of the cascaded CSC-based offshore wind farm when one or more turbines fail to operate. Simulation and experimental verification for various objectives are provided throughout the thesis. The results validate the proposed solutions for the main challenges of the cascaded current source converter based offshore wind farm.

scholarly journals A Novel Method for Navigational Risk Assessment in Wind Farm Waters Based on the Fuzzy Inference System

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Peilin Lv ◽  
Rong Zhen ◽  
Zheping Shao
Keyword(s):  
Risk Assessment ◽  
Fuzzy Inference System ◽  
Wind Farm ◽  
Fuzzy Inference ◽  
Wind Farms ◽  
Offshore Wind ◽  
Offshore Wind Farm ◽  
Offshore Wind Farms ◽  
Inference System ◽  
Natural Factors

Offshore wind power is an effective way to solve the energy crisis problem and achieve sustainable economic development. Aiming at the problems that the navigational risk of ships in the waters of offshore wind farms is difficult to quantify due to complex factors, this paper proposes a method of navigational risk assessment in the waters of offshore wind farms based on a fuzzy inference system. Firstly, through the analysis of the factors affecting the navigation system of wind farm waters, it is found that the navigational risk is affected by natural factors and navigational environment factors. Then, the visibility, the number of traffic flows, the number of encounter areas, and the distance between the sailing route and the wind farm are extracted to evaluate the risk of natural factors and the risk of the sailing environment in the navigation system of the wind farm waters, respectively. Considering the mutual influence of the factors, the fuzzy inference rules of navigational risk influence are established according to the expert experience, and a method of navigational risk assessment based on the fuzzy inference system in offshore wind farm waters is developed. In order to verify the effectiveness of the proposed method, a comprehensive evaluation of the navigational risk of wind farm waters in Changle offshore sea of Fujian Province is carried out, and the evaluation results are consistent with the actual situation. The proposed method has important theoretical significance for the navigational safety supervision of offshore wind farm waters.

scholarly journals Recovery processes in a large offshore wind farm

2021 ◽  
Vol 6 (5) ◽  
pp. 1089-1106
Author(s):  
Tanvi Gupta ◽  
Somnath Baidya Roy
Keyword(s):  
Wind Speed ◽  
Wind Farm ◽  
Wind Farms ◽  
Offshore Wind ◽  
High Wind ◽  
Offshore Wind Farm ◽  
High Wind Speed ◽  
Recovery Processes ◽  
Speed Range

Abstract. Wind turbines in a wind farm extract energy from the atmospheric flow and convert it into electricity, resulting in a localized momentum deficit in the wake that reduces energy availability for downwind turbines. Atmospheric momentum convergence from above, below, and the sides into the wakes replenishes the lost momentum, at least partially, so that turbines deep inside a wind farm can continue to function. In this study, we explore recovery processes in a hypothetical offshore wind farm with particular emphasis on comparing the spatial patterns and magnitudes of horizontal- and vertical-recovery processes and understanding the role of mesoscale processes in momentum recovery in wind farms. For this purpose, we use the Weather Research and Forecasting (WRF) model, a state-of-the-art mesoscale model equipped with a wind turbine parameterization, to simulate a hypothetical large offshore wind farm with different wind turbine spacings under realistic initial and boundary conditions. Different inter-turbine spacings range from a densely packed wind farm (case I: low inter-turbine distance of 0.5 km ∼ 5 rotor diameter) to a sparsely packed wind farm (case III: high inter-turbine distance of 2 km ∼ 20 rotor diameter). In this study, apart from the inter-turbine spacings, we also explored the role of different ranges of background wind speeds over which the wind turbines operate, ranging from a low wind speed range of 3–11.75 m s−1 (case A) to a high wind speed range of 11–18 m s−1 (case C). Results show that vertical turbulent transport of momentum from aloft is the main contributor to recovery in wind farms except in cases with high-wind-speed range and sparsely packed wind farms, where horizontal advective momentum transport can also contribute equally. Vertical recovery shows a systematic dependence on wind speed and wind farm density that is quantified using low-order empirical equations. Wind farms significantly alter the mesoscale flow patterns, especially for densely packed wind farms under high-wind-speed conditions. In these cases, the mesoscale circulations created by the wind farms can transport high-momentum air from aloft into the atmospheric boundary layer (ABL) and thus aid in recovery in wind farms. To the best of our knowledge, this is one of the first studies to look at wind farm replenishment processes under realistic meteorological conditions including the role of mesoscale processes. Overall, this study advances our understanding of recovery processes in wind farms and wind farm–ABL interactions.

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