Offshore Wind Turbine Monopile Foundation Systems in Multilayered Soil Strata under Aerodynamic and Hydrodynamic Loads: State-of-the-Art Review

Arvan, Prakash Ankitha; Raju, Rahul Dev; Arockiasamy, M.

doi:10.1061/ppscfx.sceng-1198

Cited by 8 publications

(2 citation statements)

References 50 publications

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“…For the past few years, the development of offshore wind power in China has become increasingly attractive because of abundant, steady and strong wind resources and available spaces for offshore wind turbine installation in the southeast coast to meet the strong power needs in most electricity-poor developed cities. Traditional steel towers have been widely employed in offshore wind turbine support structures with various substructures, such as monopile, jacket, and gravity base foundations [1,2]. Wang [3] presented a transformed linear Gaussian model for generating equivalent "nonlinear" irregular waves to assess the mechanical responses of an offshore jacket wind turbine support.…”

Section: Introductionmentioning

confidence: 99%

Optimization for Offshore Prestressed Concrete–Steel Hybrid Wind Turbine Support Structure with Pile Foundation Using a Parallel Modified Particle Swarm Algorithm

Li,

Xu,

Yuan

2024

JMSE

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The prestressed concrete–steel hybrid (PCSH) support structure, which replaces the lower part of the traditional support with a concrete segment, is a prospective support structure solution for ultrahigh wind turbines. Taking a 5.5 MW wind turbine support structure founded on a jacket substructure with pile foundation as an example, an optimized design of the corresponding PCSH support structure with pile foundation for offshore wind turbine is conducted considering the soil–structure interaction (SSI) and the effect of water pressure. The construction cost of the proposed structure is treated as the objective function and minimized with a parallel modified particle swarm optimization (PMPSO) algorithm where the physical dimensions of each part of the PCSH wind turbine support structure are treated as optimization variables. Eleven optimization constraints are considered under both the serviceability limit state (SLS) and the ultimate limit state (ULS) according to relevant specifications and industry standards. A penalty function strategy is introduced to make sure that these constraints are fulfilled. The mechanical behavior and the cost of the optimal PCSH support structure with pile foundation are analyzed and are compared with those of the original design with a traditional steel tube tower founded on a jacket substructure. The results show that the cost and levelized cost of energy (LCOE), a comprehensive evaluation, of the optimized PCSH support decrease obviously with the PMPSO algorithm, which can provide advanced mechanic behavior including natural frequency, top deformation, and anti-overturning capacity. Compared with the PSO algorithm, the PMPSO algorithm has better performance in the procedure of PCSH support for offshore wind turbine optimization.

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Section: Introductionmentioning

confidence: 99%

Optimization for Offshore Prestressed Concrete–Steel Hybrid Wind Turbine Support Structure with Pile Foundation Using a Parallel Modified Particle Swarm Algorithm

Li,

Xu,

Yuan

2024

JMSE

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show abstract

“…Therefore, they are highly susceptible to derailment and overturning [3][4][5], which can lead to significant casualties and huge economic losses [6,7]. Meanwhile, safety and reliability of structures (e.g., bridges and transmission Lines) under the action of strong winds have also become an increasingly serious topic [8][9][10][11][12][13][14][15]. One of effective methods to addressing this predicament is the accurate prediction of strong winds (i.e., short-term wind speed prediction), which will enable the railway dispatching system to adjust dispatch plans in a timely manner [16].…”

Section: Introductionmentioning

confidence: 99%

An Improved Ensemble-Strategy-Assisted Wind Speed Prediction Method for Railway Strong Wind Warnings

Liu,

Cui,

Cheng

et al. 2023

Atmosphere

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Reliable short-term wind speed prediction is one of the core technologies in the strong wind warning system for railway applications, which is of great significance for ensuring the safety of high-speed train operations and ancillary railway facilities. To improve forecasting accuracy, decomposition-based methods have attracted extensive attention due to their superior ability to address complex data characteristics (e.g., nonstationarity and nonlinearity). Currently, there are two pre-processing schemes for decomposition-based methods, i.e., one-time decomposition and real-time decomposition. In order to apply them better, this paper first expounds the difference between them, based on a combination of DWT (discrete wavelet transform) and CKDE (conditional kernel density estimation). The results show that although the one-time decomposition-based method has an unexceptionable accuracy, it only can provide offline prediction and thus may not be practical. The real-time decomposition-based method possesses stronger practicability and is able to provide online prediction, but it has limited accuracy. Then, an improved ensemble strategy is developed by optimizing the selection of appropriate decomposed components to conduct the prediction on the basis of real-time decomposition. This improved ensemble strategy provides an effective guidance for this selective combination, including taking historical information into consideration in the data. Finally, numerical examples and practicality analysis using two groups of measured wind speed data demonstrate that the proposed method is effective in providing high-precision online wind speed prediction. For example, compared with CKDE, the average degrees of improvement achieved by the proposed method in terms of MAE, RMSE, and MRPE, are 16.25%, 17.66%, and 16.93, respectively, while those compared with the traditional real-time decomposition method are 17.11%, 18.54%, and 16.84, respectively.

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A multiple model type-3 fuzzy control for offshore wind turbines using the Active Rotary Inertia Driver (ARID)

Zhang,

Liu,

Liu

et al. 2024

Ocean Engineering

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Offshore Wind Turbine Monopile Foundation Systems in Multilayered Soil Strata under Aerodynamic and Hydrodynamic Loads: State-of-the-Art Review

Cited by 8 publications

References 50 publications

Optimization for Offshore Prestressed Concrete–Steel Hybrid Wind Turbine Support Structure with Pile Foundation Using a Parallel Modified Particle Swarm Algorithm

Optimization for Offshore Prestressed Concrete–Steel Hybrid Wind Turbine Support Structure with Pile Foundation Using a Parallel Modified Particle Swarm Algorithm

An Improved Ensemble-Strategy-Assisted Wind Speed Prediction Method for Railway Strong Wind Warnings

A multiple model type-3 fuzzy control for offshore wind turbines using the Active Rotary Inertia Driver (ARID)

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