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Your search keyword '"off-shore wind farms (OSWFs)"' showing total 4 results
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4 results on '"off-shore wind farms (OSWFs)"'

1. Assessment and Nonlinear Modeling of Wave, Tidal and Wind Energy Converters and Turbines.

Author

Karimirad, Madjid, Collu, Maurizio, and Karimirad, Madjid

Subjects
History of engineering & technology, 10 MW wind turbines, AFWT, ANSYS CFX, Extreme Learning Machine (ELM), FOWT, Kirsten-Boeing, OWC, Tensorflow, air compressibility, air turbine, blade back twist, blade flapwise moment, caisson breakwater application, cylinder wake, dynamic analysis, energy converter, energy harnessing, fatigue life assessment, flexible power cables, floating offshore wind turbine, floating offshore wind turbine (FOWT), floating offshore wind turbines, flow-induced oscillations, flow-structure interaction, flower pollination algorithm (FPA), frequency domain model, hill-climbing method, hydrodynamics, inclined columns, integral length scales, large floating platform, large-eddy simulation (LES), maximum power point tracking (MPPT), metamodeling, multi-segmented mooring line, negative damping, neural nets, off-shore wind farms (OSWFs), optimization, oscillating water column, parametric study, pitch-to-stall, platform optimization, point-absorbing, power take-off (PTO), semi-submersible, semisubmersible platform, site assessment, tank testing, tidal energy, tower axial fatigue life, tower fore-aft moments, turbulence, valves, vertical axis turbine, vortex shedding, vortex-induced vibration, wake model, wave energy, wave energy converter (WEC), wave power converting system, wave-current interaction, wave-to-wire model, wave-turbulence decomposition, wind energy, wind power (WP), wind turbine (WT)
Abstract

Summary: The Special Issue "Assessment and Nonlinear Modeling of Wave, Tidal, and Wind Energy Converters and Turbines" contributes original research to stimulate the continuing progress of the offshore renewable energy (ORE) field, with a focus on state-of-the-art numerical approaches developed for the design and analysis of ORE devices. Particularly, this collection provides new methodologies, analytical/numerical tools, and theoretical methods that deal with engineering problems in the ORE field of wave, wind, and current structures. This Special Issue covers a wide range of multidisciplinary aspects, such as the 1) study of generalized interaction wake model systems with elm variation for offshore wind farms; 2) a flower pollination method based on global maximum power point tracking strategy for point-absorbing type wave energy converters; 3) performance optimization of a Kirsten-Boeing turbine using a metamodel based on neural networks coupled with CFD; 4) proposal of a novel semi-submersible floating wind turbine platform composed of inclined columns and multi-segmented mooring lines; 5) reduction of tower fatigue through blade back twist and active pitch-to-stall control strategy for a semi-submersible floating offshore wind turbine; 6) assessment of primary energy conversion of a closed-circuit OWC wave energy converter; 7) development and validation of a wave-to-wire model for two types of OWC wave energy converters; 8) assessment of a hydrokinetic energy converter based on vortex-induced angular oscillations of a cylinder; 9) application of wave-turbulence decomposition methods on a tidal energy site assessment; 10) parametric study for an oscillating water column wave energy conversion system installed on a breakwater; 11) optimal dimensions of a semisubmersible floating platform for a 10 MW wind turbine; 12) fatigue life assessment for power cables floating in offshore wind turbines.

2. Study of Generalized Interaction Wake Models Systems with ELM Variation for Off-Shore Wind Farms

Author

Mingcan Li, Hanbin Xiao, Lin Pan, and Chengjun Xu

Subjects
off-shore wind farms (OSWFs), wake model, wind turbine (WT), Extreme Learning Machine (ELM), wind power (WP), large-eddy simulation (LES), Technology
Abstract

This paper reports a novel frandsen generalized wake model and its variation model-frandsen generalized normal distribution wake model for off-shore wind farms. Two different new wake models in off-shore wind farms have been studied comparatively. Their characteristics have been analyzed through mathematical modeling and derivation. Meanwhile, simulation experiments show that the proposed two new wake models have different properties. Furthermore, the distributions of wind speed and wind direction are modeled by the statistical methods and Extreme Learning Machine through the off-shore wind farms of Yangshan Deepwater Harbor in the Port of Shanghai, China. In addition, the data of wind energy are provided to verify and test the correctness and effectiveness of the proposed two models. Wind power has been demonstrated by wind rose and wind resources with real-time data. These techniques contribute to enhance planning, utilization and exploitation for wind power of off-shore wind farms.

Published
2019
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3. Study of Generalized Interaction Wake Models Systems with ELM Variation for Off-Shore Wind Farms

Author

Hanbin Xiao, Mingcan Li, Lin Pan, and Chengjun Xu

Subjects
Control and Optimization, Meteorology, Energy Engineering and Power Technology, Variation (game tree), Wake, wind turbine (WT), lcsh:Technology, Wind speed, large-eddy simulation (LES), off-shore wind farms (OSWFs), Electrical and Electronic Engineering, Engineering (miscellaneous), Generalized normal distribution, Extreme learning machine, Shore, geography, Wind power, geography.geographical_feature_category, Extreme Learning Machine (ELM), lcsh:T, Renewable Energy, Sustainability and the Environment, business.industry, wind power (WP), Wind direction, Environmental science, wake model, business, Energy (miscellaneous)
Abstract

This paper reports a novel frandsen generalized wake model and its variation model-frandsen generalized normal distribution wake model for off-shore wind farms. Two different new wake models in off-shore wind farms have been studied comparatively. Their characteristics have been analyzed through mathematical modeling and derivation. Meanwhile, simulation experiments show that the proposed two new wake models have different properties. Furthermore, the distributions of wind speed and wind direction are modeled by the statistical methods and Extreme Learning Machine through the off-shore wind farms of Yangshan Deepwater Harbor in the Port of Shanghai, China. In addition, the data of wind energy are provided to verify and test the correctness and effectiveness of the proposed two models. Wind power has been demonstrated by wind rose and wind resources with real-time data. These techniques contribute to enhance planning, utilization and exploitation for wind power of off-shore wind farms.

Published
2019

4. Study of Generalized Interaction Wake Models Systems with ELM Variation for Off-Shore Wind Farms.

Author

Li, Mingcan, Xiao, Hanbin, Pan, Lin, and Xu, Chengjun

Subjects
*WIND power plants, *MACHINE learning, *WIND power, *MATHEMATICAL models, *SIMULATION methods & models
Abstract

This paper reports a novel frandsen generalized wake model and its variation model-frandsen generalized normal distribution wake model for off-shore wind farms. Two different new wake models in off-shore wind farms have been studied comparatively. Their characteristics have been analyzed through mathematical modeling and derivation. Meanwhile, simulation experiments show that the proposed two new wake models have different properties. Furthermore, the distributions of wind speed and wind direction are modeled by the statistical methods and Extreme Learning Machine through the off-shore wind farms of Yangshan Deepwater Harbor in the Port of Shanghai, China. In addition, the data of wind energy are provided to verify and test the correctness and effectiveness of the proposed two models. Wind power has been demonstrated by wind rose and wind resources with real-time data. These techniques contribute to enhance planning, utilization and exploitation for wind power of off-shore wind farms. [ABSTRACT FROM AUTHOR]

Published
2019
Full Text
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