Automatic Segregation of Pelagic Habitats

It remains difficult to segregate pelagic habitats since structuring processes are dynamic on a wide range of scales and clear boundaries in the open ocean are non-existent. However, to improve our knowledge about existing ecological niches and the processes shaping the enormous diversity of marine plankton, we need a better understanding of the driving forces behind plankton patchiness. Here we describe a new machine-learning method to detect and quantify pelagic habitats based on hydrographic measurements. An Autoencoder learns two-dimensional, meaningful representations of higher-dimensional micro-habitats, which are characterized by a variety of biotic and abiotic measurements from a high-speed ROTV. Subsequently, we apply a density-based clustering algorithm to group similar micro-habitats into associated pelagic macro-habitats in the German Bight of the North Sea. Three distinct macro-habitats, a “surface mixed layer,” a “bottom layer,” and an exceptionally “productive layer” are consistently identified, each with its distinct plankton community. We provide evidence that the model detects relevant features like the doming of the thermocline within an Offshore Wind Farm or the presence of a tidal mixing front.

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Regional Impacts of Offshore Windfarming on the Hydro- and Ecosystem Dynamics in the North Sea

10.5194/egusphere-egu2020-7997 ◽

2020 ◽

Author(s):

Nils Christiansen ◽

Ute Daewel ◽

Corinna Schrum ◽

Jeff Carpenter ◽

Bughsin Djath ◽

...

Keyword(s):

North Sea ◽

Wind Farm ◽

Ecosystem Dynamics ◽

Ocean Dynamics ◽

Offshore Wind ◽

Tidal Mixing ◽

Surface Boundary ◽

The North ◽

Wake Effects ◽

The North Sea

<p>The production of renewable offshore wind energy in the North Sea increases rapidly, including development in ecologically significant regions. Recent studies identified implications like large-scale wind wake effects and mixing of the water column induced by wind turbines foundations. Depending on atmospheric stability, wind wakes imply changes in momentum flux and increased turbulence up to 70 km downstream, affecting the local conditions (e.g. wind speed, cloud development) near offshore wind farms. Atmospheric wake effects likely translate to the sea-surface boundary layer and hence influence vertical transport in the surface mixing layer. Changes in ocean stratification raise concerns about substantial consequences for local hydrodynamic and biogeochemical processes as well as for the marine ecosystem.<br>Using newly developed wind wake parametrisations together with the unstructured-grid model SCHISM and the biogeochemistry model ECOSMO, this study addresses windfarming impacts in the North Sea for future offshore wind farm scenarios. We focus on wind wake implications on ocean dynamics as well as on changes in tidal mixing fronts near the Dogger Bank and potential ecological consequences. At this, we create important knowledge on how the cross-scale wind farm impacts can be modelled suitably on the system scale.</p>

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Wind Farm Layout Upgrade Optimization

Energies ◽

10.3390/en12132465 ◽

2019 ◽

Vol 12 (13) ◽

pp. 2465 ◽

Cited By ~ 1

Author(s):

Mamdouh Abdulrahman ◽

David Wood

Keyword(s):

Energy Production ◽

Wind Farm ◽

Lower Cost ◽

Wind Farms ◽

Offshore Wind ◽

General Representation ◽

Thrust Coefficient ◽

Wind Farm Layout ◽

Cost Of Energy ◽

Wide Range

The problem of optimally increasing the size of existing wind farms has not been investigated in the literature. In this paper, a proposed wind farm layout upgrade by adding different (in type and/or hub height) commercial turbines to an existing farm is introduced and optimized. Three proposed upgraded layouts are considered: internal grid, external grid, and external unstructured. The manufacturer’s power curve and a general representation for thrust coefficient are used in power and wake calculations, respectively. A simple field-based model is implemented and both offshore and onshore conditions are considered. A genetic algorithm is used for the optimization. The trade-off range between energy production and cost of energy is investigated by considering three objective functions, individually: (1) annual energy production; (2) cost of added energy; and (3) cost of total energy. The proposed upgraded layouts are determined for the Horns Rev 1 offshore wind farm. The results showed a wide range of suitable upgrade scenarios depending on the upgraded layout and the optimization objective. The farm energy production is increased by 190–336% with a corresponding increase in the total cost by 147–720%. The external upgrade offers more energy production but with much more cost. The unstructured layouts showed clear superiority over the grid ones by providing much lower cost of energy.

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Full-scale Performance of the SES Motion Control System

10.5957/fast-2015-006 ◽

2015 ◽

Author(s):

Øyvind F. Auestad ◽

J. William McFann ◽

Jan T. Gravdahl

Keyword(s):

Control System ◽

Motion Control ◽

High Speed ◽

Wind Farm ◽

Surface Effect ◽

Vertical Plane ◽

Full Scale ◽

Offshore Wind ◽

Motion Control System ◽

Air Cushion

The pressurized air cushion on a Surface Effect Ship (SES) can lift up to 80% of total vessel mass. The SES Motion Control System (SES-MCS) controls the vent valves which again controls the air cushion pressure, assuming lift fan air flow is pressurizing the air cushion. By controlling the air cushion pressure one can significantly counteract vertical sea wave disturbances, ensure high passenger comfort and reduce sea-sickness. The case studied in this work is the Umoe Mandal Wave Craft prototype, ’Umoe Ventus’, which is a high-speed offshore wind-farm service vessel specially designed for control in the vertical plane. The SES-MCS can adjust the draft from 1m to 3.2m in less time than the wave period. The SES-MCS can reduce motions significantly in order to perform Operation and Maintenance (O&M) in high seas. The craft is the fastest wind-farm service vessel of its size with high comfort in all relevant sea states. The performance of the SES-MCS is demonstrated through full-scale sea trials.

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The Equivalent Model for PMSG Offshore Wind Farm Based on Clustering Algorithm

2021 6th Asia Conference on Power and Electrical Engineering (ACPEE) ◽

10.1109/acpee51499.2021.9436859 ◽

2021 ◽

Author(s):

Xuan Li ◽

Ying Xu ◽

Fengmo Li ◽

Qiao Kang ◽

Zhuling Li ◽

...

Keyword(s):

Wind Farm ◽

Clustering Algorithm ◽

Offshore Wind ◽

Equivalent Model ◽

Offshore Wind Farm

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Comparison of Wake Model Simulations with Offshore Wind Turbine Wake Profiles Measured by Sodar

Journal of Atmospheric and Oceanic Technology ◽

10.1175/jtech1886.1 ◽

2006 ◽

Vol 23 (7) ◽

pp. 888-901 ◽

Cited By ~ 173

Author(s):

R. J. Barthelmie ◽

G. C. Larsen ◽

S. T. Frandsen ◽

L. Folkerts ◽

K. Rados ◽

...

Keyword(s):

Wind Speed ◽

Wind Turbine ◽

Wind Farm ◽

Offshore Wind ◽

Superior Performance ◽

Offshore Wind Turbine ◽

Wide Range ◽

Wake Model ◽

Method Of Evaluation ◽

Turbine Wake

Abstract This paper gives an evaluation of most of the commonly used models for predicting wind speed decrease (wake) downstream of a wind turbine. The evaluation is based on six experiments where free-stream and wake wind speed profiles were measured using a ship-mounted sodar at a small offshore wind farm. The experiments were conducted at varying distances between 1.7 and 7.4 rotor diameters downstream of the wind turbine. Evaluation of the models compares the predicted and observed velocity deficits at hub height. A new method of evaluation based on determining the cumulative momentum deficit over the profiles is described. Despite the apparent simplicity of the experiments, the models give a wide range of predictions. Overall, it is not possible to establish any of the models as having individually superior performance with respect to the measurements.

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Wind Turbine Clustering Algorithm of Large Offshore Wind Farms considering Wake Effects

Mathematical Problems in Engineering ◽

10.1155/2019/6874693 ◽

2019 ◽

Vol 2019 ◽

pp. 1-7 ◽

Cited By ~ 1

Author(s):

Binbin Zhang ◽

Jun Liu

Keyword(s):

Wind Turbines ◽

Wind Farm ◽

Clustering Algorithm ◽

Wind Farms ◽

Offshore Wind ◽

Offshore Wind Farm ◽

Wake Effect ◽

Offshore Wind Farms ◽

Power Simulation ◽

Value Decomposition

This paper proposed the SVD (singular value decomposition) clustering algorithm to cluster wind turbines into some group for a large offshore wind farm, in order to reduce the high-dimensional problem in wind farm power control and numerical simulation. Firstly, wind farm wake relationship matrixes are established considering the wake effect in an offshore wind farm, and the SVD of wake relationship matrixes is used to cluster wind turbines into some groups by the fuzzy clustering algorithm. At last, the Horns Rev offshore wind farm is analyzed to test the clustering algorithm, and the clustering result and the power simulation show the effectiveness and feasibility of the proposed clustering strategy.

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Correlations of power output fluctuations in an offshore wind farm using high-resolution SCADA data

10.5194/wes-2020-96 ◽

2020 ◽

Author(s):

Janna K. Seifert ◽

Martin Kraft ◽

Martin Kühn ◽

Laura J. Lukassen

Keyword(s):

Wind Turbine ◽

Wind Turbines ◽

Power Output ◽

Wind Farm ◽

Clustering Algorithm ◽

Free Field ◽

Measurement Data ◽

Offshore Wind ◽

Offshore Wind Farm ◽

Output Fluctuations

Abstract. The correlation of power output fluctuations of wind turbines in free field are investigated, taking into account the challenge of varying correlation states due to variable flow and wind turbine conditions within the wind farm. Based on eight months of 1 Hz SCADA data, measured at an offshore wind farm with 80 wind turbines, the influence of different parameters on the correlation of power output fluctuations is analysed. It is found that the correlation of power output fluctuations of wind turbines depends on the location of the wind turbines within the wind farm as well as the inflow conditions (free-stream or wake). Wind direction investigations show that the correlation is highest for streamwise aligned pairs and decreases towards spanwise pairs. Most importantly, the highly variable measurement data in a free-field wind farm has considerable influence on the identification of different correlation states. To account for that, the clustering algorithm k-means is used to group wind turbine pairs with similar correlations. The main outcome is that next to the location of a wind turbine pair in the wind farm the standard deviation in their power output and their power differences are suitable parameters to describe the correlation of power output fluctuations.

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Micrositing Optimization of the Block Island Wind Farm, RI, USA

Volume 8: Ocean Renewable Energy ◽

10.1115/omae2013-10191 ◽

2013 ◽

Cited By ~ 1

Author(s):

Christopher M. O’Reilly ◽

Annette R. Grilli ◽

Gopu R. Potty

Keyword(s):

Renewable Energy ◽

Objective Function ◽

Rhode Island ◽

Wind Farm ◽

Clustering Algorithm ◽

Cost Model ◽

Wind Farms ◽

Offshore Wind ◽

Mitigating Factors ◽

Initial Objective

The Rhode Island Ocean Special Area Management Plan (RIOSAMP) has been implemented in Rhode Island since 2008 to provide guidance to local regulators in the zoning of renewable energy, with a focus on the siting of offshore wind farms. The project culminated in the siting of the first North American offshore wind project, optimized using a spatial planning approach combining exclusionary and mitigating factors. The optimization of mitigating factors is based on a standard cost model approach and extended to include ecological and societal factors. This macro-siting optimization phase provided the framework to define a Renewable Energy Zone (REZ) for wind farm development and the present study seeks the siting optimization of the wind farm layout within this zone. The optimization considers the loss in power resulting from turbine wake interaction, a cable cost clustering algorithm, and the spatial variation of both foundation cost and the available wind resource within the REZ through a micrositing objective function. This initial objective function is extended to include ecological and social costs. The layout optimization is based on a Genetic Algorithm (GA) optimization scheme. The method is applied to the REZ area, demonstrating that a gain of approximately $10 million over 20 years could be obtained if an “optimal layout” would be selected over the initial layout chosen by the developers.

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Microfabrication research at the National Submicron Facility

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100074331 ◽

1983 ◽

Vol 41 ◽

pp. 86-89

Author(s):

E.D. Wolf

Keyword(s):

Integrated Circuits ◽

Particle Physics ◽

High Speed ◽

New Technology ◽

High Energy ◽

High Energy Particle ◽

New Science ◽

Wide Range ◽

Intermediate Domain ◽

Circuit Function

Most microelectronics devices and circuits operate faster, consume less power, execute more functions and cost less per circuit function when the feature-sizes internal to the devices and circuits are made smaller. This is part of the stimulus for the Very High-Speed Integrated Circuits (VHSIC) program. There is also a need for smaller, more sensitive sensors in a wide range of disciplines that includes electrochemistry, neurophysiology and ultra-high pressure solid state research. There is often fundamental new science (and sometimes new technology) to be revealed (and used) when a basic parameter such as size is extended to new dimensions, as is evident at the two extremes of smallness and largeness, high energy particle physics and cosmology, respectively. However, there is also a very important intermediate domain of size that spans from the diameter of a small cluster of atoms up to near one micrometer which may also have just as profound effects on society as “big” physics.

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