scholarly journals Development of Seismic Demand for Chang-Bin Offshore Wind Farm in Taiwan Strait

Energies ◽  
2016 ◽  
Vol 9 (12) ◽  
pp. 1036 ◽  
Author(s):  
Yu-Kai Wang ◽  
Juin-Fu Chai ◽  
Yu-Wen Chang ◽  
Ti-Ying Huang ◽  
Yu-Shu Kuo
Keyword(s):  
Wind Farm ◽  
Taiwan Strait ◽  
Offshore Wind ◽  
Seismic Demand ◽  
Offshore Wind Farm

Noise field characterization in the habitat of the East Taiwan Strait Indo-Pacific Humpback Dolphin during the pile driving activity of demonstration offshore wind farm

2016 ◽  
Vol 140 (4) ◽  
pp. 3022-3022 ◽  
Author(s):  
Chi-Fang Chen ◽  
Shane Guan ◽  
Lien-Sian Chou ◽  
Ruey Chang Wei ◽  
William W. Hu ◽  
...  
Keyword(s):  
Wind Farm ◽  
Taiwan Strait ◽  
Offshore Wind ◽  
Pile Driving ◽  
Offshore Wind Farm ◽  
Noise Field

scholarly journals A Methodology for Estimating the Position of the Engineering Bedrock for Offshore Wind Farm Seismic Demand in Taiwan

Energies ◽  
2021 ◽  
Vol 14 (9) ◽  
pp. 2474
Author(s):  
Yu-Shu Kuo ◽  
Tzu-Ling Weng ◽  
Hui-Ting Hsu ◽  
Hsing-Wei Chang ◽  
Yun-Chen Lin ◽  
...  
Keyword(s):  
Shear Wave ◽  
Wave Velocity ◽  
Shear Wave Velocity ◽  
Wind Farm ◽  
Offshore Wind ◽  
Seismic Demand ◽  
Ground Model ◽  
Offshore Wind Farm ◽  
Offshore Wind Turbines ◽  
Local Code

Taiwan lies in the circum-Pacific earthquake zone. The seabed soil of offshore wind farms in Taiwan is mainly composed of loose silty sand and soft, low-plasticity clay. The seismic demand for offshore wind turbines has been given by the local code. Ground-motion analysis is required to consider the site effects of the soil liquefaction potential evaluation and the foundation design of offshore wind turbines. However, the depth of the engineering bedrock for ground motion analysis is not presented in the local code. In this study, we develop a three-dimensional ground model of an offshore wind farm in the Changhua area, through use of collected in situ borehole and PS (P wave (compression) and S (shear) wave velocities) logging test data. The engineering bedrock is the sediment at the depth where the average shear wave velocity of soil within 30 m, Vsd30, is larger than 360 m/s. In this ground model, the shear wave velocity of each type of soil is quantified using the seismic empirical formulation developed in this study. The results indicate that the engineering bedrock lies at least 49.5–83 m beneath the seabed at the Changhua offshore wind farm. Based on these findings, it is recommended that drilling more than 100 m below the seabed be done to obtain shear wave velocity data for a ground response analysis of the seismic force assessment of offshore wind farm foundation designs.

Seismic prediction of soil distribution for the Chang-Bin offshore wind farm in the Taiwan Strait

2020 ◽  
Vol 8 (4) ◽  
pp. T727-T737
Author(s):  
Wei-Chung Han ◽  
Yi-Wei Lu ◽  
Sheng-Chung Lo
Keyword(s):  
Neural Network ◽  
Wind Farm ◽  
Taiwan Strait ◽  
Offshore Wind ◽  
Offshore Wind Farm ◽  
Seismic Profiles ◽  
Borehole Data ◽  
The Taiwan Strait ◽  
Soil Distribution

Direct soil measurements are limited to borehole locations and are therefore sparse in the oceans. To effectively characterize the soil distributions for the Chang-Bin offshore wind farm, which is an area with the greatest wind energy potential in the Taiwan Strait, we have developed a workflow to predict the soil distribution in the subsurface based on integrated analysis of seismic data and borehole data. First, we characterize the key seismic units and their seismic response in order to understand the regional stratigraphy. Then, we correlate the soil types to each stratigraphic unit as the constraint for the input and quality control to train a neural network based on seismic multiattribute analysis. Finally, we develop a neural network that is suitable for soil prediction in the Chang-Bin offshore wind farm. Five seismic units identified from the seismic profiles reveal that the regional stratigraphy has been greatly affected by sea-level change and the sediment transportation process. Confirmed by independent in situ borehole data, the neural network is considered reliable up to 60 m below the seafloor, whereas decreased signal-to-noise ratios at greater depths lead to poorer prediction accuracy. Compared to previous studies that mainly are based on high-quality 3D seismic and well logging data, our method can predict the soil distribution by analyzing 2D seismic profiles and simplified soil layers alone. The prediction results reveal detailed lithologic variations that are tested by in situ borehole measurements. Therefore, we are confident that this approach could effectively obtain the soil distribution prediction and thus reduce the costs in offshore engineering applications.

Damping of Frequency Fluctuations of Hybrid Power System by Variable Deloaded Operation of PMSG Based Offshore Wind Farm

2019 ◽  
Vol 139 (4) ◽  
pp. 259-268
Author(s):  
Effat Jahan ◽  
Md. Rifat Hazari ◽  
Mohammad Abdul Mannan ◽  
Atsushi Umemura ◽  
Rion Takahashi ◽  
...  
Keyword(s):  
Power System ◽  
Wind Farm ◽  
Offshore Wind ◽  
Offshore Wind Farm ◽  
Hybrid Power System ◽  
Hybrid Power ◽  
Frequency Fluctuations

scholarly journals Bidirectional marx DC–DC converter for offshore wind farm application

2019 ◽  
Vol 2019 (17) ◽  
pp. 3848-3854
Author(s):  
Samir Milad Alagab ◽  
Sarath Tennakoon ◽  
Chris Gould
Keyword(s):  
Wind Farm ◽  
Offshore Wind ◽  
Offshore Wind Farm

Offshore wind farm site selection using interval rough numbers based Best Worst Method and MARCOS

2021 ◽  
pp. 107532
Author(s):  
Muhammet Deveci ◽  
Ender Özcan ◽  
Robert John ◽  
Dragan Pamucar ◽  
Himmet Karaman
Keyword(s):  
Site Selection ◽  
Wind Farm ◽  
Offshore Wind ◽  
Offshore Wind Farm ◽  
Best Worst Method

scholarly journals Simulation analysis and suppression measures of harmonic resonance of offshore wind farm

2021 ◽  
Vol 1754 (1) ◽  
pp. 012153
Author(s):  
YAN Quanchun ◽  
GU Wen ◽  
LIU Yanan ◽  
LI Chenglong ◽  
WU Tao
Keyword(s):  
Wind Farm ◽  
Simulation Analysis ◽  
Offshore Wind ◽  
Offshore Wind Farm ◽  
Harmonic Resonance
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