Acquisition and accuracy evaluation of offshore wind data by lattice tower mast

2020 ◽  
Vol 2020 (0) ◽  
pp. 16B13
Author(s):  
Nobutoshi NISHIO ◽  
Satoshi NAKASHIMA ◽  
Tetsuro TAKAGI ◽  
Makoto Iida
Keyword(s):  
Offshore Wind ◽  
Wind Data ◽  
Accuracy Evaluation ◽  
Lattice Tower

scholarly journals Offshore Wind Energy Resource Assessment across the Territory of Oman: A Spatial-Temporal Data Analysis

2021 ◽  
Vol 13 (5) ◽  
pp. 2862
Author(s):  
Amer Al-Hinai ◽  
Yassine Charabi ◽  
Seyed H. Aghay Kaboli
Keyword(s):  
Data Analysis ◽  
Wind Energy ◽  
Wind Turbines ◽  
Energy Resource ◽  
Wind Farms ◽  
Offshore Wind ◽  
Wind Data ◽  
Offshore Wind Energy ◽  
Offshore Wind Turbines ◽  
Wind Potential

Despite the long shoreline of Oman, the wind energy industry is still confined to onshore due to the lack of knowledge about offshore wind potential. A spatial-temporal wind data analysis is performed in this research to find the locations in Oman’s territorial seas with the highest potential for offshore wind energy. Thus, wind data are statistically analyzed for assessing wind characteristics. Statistical analysis of wind data include the wind power density, and Weibull scale and shape factors. In addition, there is an estimation of the possible energy production and capacity factor by three commercial offshore wind turbines suitable for 80 up to a 110 m hub height. The findings show that offshore wind turbines can produce at least 1.34 times more energy than land-based and nearshore wind turbines. Additionally, offshore wind turbines generate more power in the Omani peak electricity demand during the summer. Thus, offshore wind turbines have great advantages over land-based wind turbines in Oman. Overall, this work provides guidance on the deployment and production of offshore wind energy in Oman. A thorough study using bankable wind data along with various logistical considerations would still be required to turn offshore wind potential into real wind farms in Oman.

scholarly journals Combining QuickSCAT wind data and Landsat ETM+ images to evaluate the offshore wind power resource of East Vietnam Sea

2020 ◽  
Vol 20 (2) ◽  
pp. 143-153
Author(s):  
Nguyen Xuan Tung ◽  
Do Huy Cuong ◽  
Bui Thi Bao Anh ◽  
Nguyen Thi Nhan ◽  
Tran Quang Son
Keyword(s):  
Power Generation ◽  
Wind Energy ◽  
Wind Power ◽  
Power Density ◽  
Geographical Location ◽  
Wind Power Generation ◽  
Offshore Wind ◽  
Wind Data ◽  
Power Capacity ◽  
Wind Power Density

Since the East Vietnam Sea has an advantageous geographical location and rich natural resources, we can develop and manage islands and reefs in this region reasonably to declare national sovereignty. Based on 1096 scenes of QuikSCAT wind data of 2006–2009, wind power density at 10 m hight is calculated to evaluate wind energy resources of the East Vietnam Sea. With a combination of wind power density at 70 m hight calculated according to the power law of wind energy profile and reef flats extracted from 35 scenes of Landsat ETM+ images, installed wind power capacity of every island or reef is estimated to evaluate wind power generation of the East Vietnam Sea. We found that the wind power density ranges from levels 4–7, so that the wind energy can be well applied to wind power generation. The wind power density takes on a gradually increasing trend in seasons. Specifically, the wind power density is lower in spring and summer, whereas it is higher in autumn and winter. Among islands and reefs in the East Vietnam Sea, the installed wind power capacity of Hoang Sa archipelago is highest in general, the installed wind power capacity of Truong Sa archipelago is at the third level. The installed wind power capacity of Discovery Reef, Bombay Reef, Tree island, Lincoln island, Woody Island of Hoang Sa archipelago and Mariveles Reef, Ladd Reef, Petley Reef, Cornwallis South Reef of Truong Sa archipelago is relatively high, and wind power generation should be developed on these islands first.

Site‐specific life cycle assessment of a pilot floating offshore wind farm based on suppliers’ data and geo‐located wind data

2020 ◽  
Vol 24 (1) ◽  
pp. 248-262 ◽  
Author(s):  
Baptiste Poujol ◽  
Anne Prieur‐Vernat ◽  
Jean Dubranna ◽  
Romain Besseau ◽  
Isabelle Blanc ◽  
...  
Keyword(s):  
Life Cycle Assessment ◽  
Life Cycle ◽  
Wind Farm ◽  
Offshore Wind ◽  
Wind Data ◽  
Offshore Wind Farm ◽  
Site Specific

Effects of different wind data sources in offshore wind power assessment

2015 ◽  
Vol 77 ◽  
pp. 101-114 ◽  
Author(s):  
Takvor H. Soukissian ◽  
Anastasios Papadopoulos
Keyword(s):  
Wind Power ◽  
Data Sources ◽  
Offshore Wind ◽  
Wind Data ◽  
Offshore Wind Power

Atmospheric Wind Data Collection and Wind Turbine Analysis in UAE

2009 ◽  
Author(s):  
Isam Janajreh ◽  
Rana Qudaih ◽  
Ilham Talab ◽  
Zaki Al Nahari
Keyword(s):  
Wind Energy ◽  
Wind Turbine ◽  
Wind Turbines ◽  
Ecological Impact ◽  
Wind Farms ◽  
Offshore Wind ◽  
Wind Data ◽  
Gulf Region ◽  
Electrical Grid ◽  
Capital Costs

Wind turbine technology has improved dramatically in the last two decades as demonstrated by their plummeting capital costs ($0.08/KW), the enhanced reliability, and the increased efficiency. Large-scale wind turbines and wind farms provided 94.1GW of electrical grid capacity in 2007, and are expected to reach 160 GW by 2010 according to WWEA. Wind energy is plentiful and sustainable energy source with an estimated potential capacity of 72 TW. In Denmark the inland and offshore implementation of wind energy generation adds 1/5 of their electrical grid capacity. In Germany, it is forecasted to attain 12.5% by early 2010. Offshore wind farms have lower ecological impact due to lack of land mortgage, easier transportation, and low perception of noise issue. In the gulf region, the generated power can fulfill the power needs of UAE’s islands, while the excess capacity can be channeled to the inland grids fulfilling the peak demand. In this work we will investigate the implementation of low-turning moment wind turbines in the UAE, suited for low wind speeds (∼3–5m/s) and that consists of two research components: (i) Collection of wind data, analysis, recommendation for implementation strategies, and using Masdar wind data to assess its characteristics and its fit for wind turbine implementation; (ii) Carry out flow analysis on a downwind, two-bladed, horizontal-axes wind turbine to investigate the flow lift, drag and wake characteristics on the tower blade interaction. The interaction is studied utilizing Arbitrary Lagrangian Eulerian method. Downwind turbines are self-aligned, pass up yaw mechanisms and its needed power, and have fewer moving parts that necessitate regular maintenance. These factors however play in favor of wind turbine that is subjected to low wind speed.

scholarly journals ASSESSMENT OF IEC 61400-1 NORMAL TURBULENCE MODEL FOR WIND CONDITIONS IN TAIWAN WEST COAST AREAS

2014 ◽  
Vol 34 ◽  
pp. 1460382 ◽  
Author(s):  
TZONG-SHYNG LEU ◽  
JUI-MING YO ◽  
YI-TING TSAI ◽  
JIU-JIH MIAU ◽  
TA-CHUNG WANG ◽  
...  
Keyword(s):  
Standard Deviation ◽  
Turbulence Model ◽  
West Coast ◽  
Wind Farms ◽  
Offshore Wind ◽  
Wind Data ◽  
Model Parameters ◽  
Observation Data ◽  
Offshore Wind Farms ◽  
Proposed Model

This paper studies the applicability of Normal Turbulence Model (NTM) in IEC61400-1 for wind conditions in Taiwan west coast area where future offshore wind farms are planning in the nearby areas. The parameters for the standard deviation of wind fluctuating [Formula: see text] are presented and compared with IEC Normal Turbulence Model. It is found that the trend of turbulence standard deviation [Formula: see text] based on the observation data agreed qualitatively well with IEC Normal Turbulence Model. However, IEC Normal Turbulence Model results in rather small [Formula: see text] compared to surveillance wind data in Taiwan. In this paper, model parameters for [Formula: see text] and [Formula: see text] based on the two-year observation wind data are proposed. The proposed model parameters a, b, α and β are 0.9125, 2.4345, 0.097 and 2.1875.

scholarly journals Wind potential assessment and optimized turbine distribution in wind farm using WAsP and WindPRO software

2020 ◽  
Vol 2 (3) ◽  
pp. 131-139
Author(s):  
Vinh Thanh Le
Keyword(s):  
Wind Farm ◽  
Offshore Wind ◽  
Wind Data ◽  
Weibull Function ◽  
Input Constraints ◽  
Short Term ◽  
Wind Potential ◽  
Wind Statistics ◽  
Scale Data

In order to develop a wind farm project, the wind potential assessment and siting wind turbine are very important. It directly impacts energy production – a huge influence on the economic efficiency of the wind farm project. So, this paper presents the method to assess wind potential and optimized turbine distribution in Vietnam's offshore wind farm site, based on data from the met mast of GIZ organization (2012 - 2017) at An Ninh Dong commune, Tuy An district, Phu Yen province. The paper presents wind statistics theory from measured data through Weibull function. Comparing the short-term and long-term wind data (from meso-scale data sources – NASA, Hydrometeorological Station ...) is done by module MCP (Measure-Correlate-Predict). Wind potential is assessed when considering the effects of elevation and terrain roughness from wind data that has been long-term adjusted through WAsP and WindPRO software. Jensen model assesses the effects of wake loss between the turbines. The method calculates the power output of the wind farm when considering the influence of turbines is presented, as well as the algorithm of optimized turbine distribution. The optimized turbine distribution is done through WindPRO software. Finally, the turbine distribution results are presented with wind potential has been assessed and the input constraints of optimization.

scholarly journals IMPROVED FORMULAS FOR ESTIMATING OFFSHORE WINDS

1984 ◽  
Vol 1 (19) ◽  
pp. 149
Author(s):  
S.A. Hsu
Keyword(s):  
Continental Shelf ◽  
Coastal Waters ◽  
Offshore Wind ◽  
Wind Data ◽  
Wind Speeds ◽  
Coastal Marine ◽  
The World ◽  
Wind Measurements ◽  
The U.S

Differences in onshore and offshore wind speeds have long been known to exist [see, e.g., (2), (15), (16)]. Marine meteorologists in the weather services are required to forecast offshore winds. Many studies related to coastal marine sciences and engineering require wind data or estimates for offshore regions. Yet in situ measurements over water are often lacking. Traditionally, wind measurements over land, preferably near coasts, have been used to estimate offshore winds. However, because simultaneous onshore and offshore observations do not always exist, systematic studies such as simple comparisons between these two environments are also lacking. Only recently the U.S. National Oceanic and Atmospheric Administration (NOAA) deployed a network of buoys for longer term measurements over the continental shelf as well as farther offshore. All of these buoys are located in or near U.S. coastal waters. However, there are still vast regions in other parts of the world where such a network does not exist.

Offshore Wind Shear Estimations for Wind Power Assessment

2008 ◽  
Author(s):  
Susan W. Stewart
Keyword(s):  
Wind Power ◽  
Wind Shear ◽  
Meteorological Data ◽  
Open Water ◽  
Offshore Wind ◽  
Wind Data ◽  
South Atlantic Bight ◽  
Data Sets ◽  
Time Period ◽  
Bodies Of Water

Appropriate wind shear estimates are extremely important when assessing any regions’ wind power resource. Wind shear is used not only to estimate wind velocity at wind turbine hub heights other than the data collection height, but also as a siting tool to compare the wind resources in different locations when wind data are not available at a consistent height. Models for wind shear over land, as well as simple models for wind shear over open water have been found to correlate poorly with offshore wind data. This is thought to be partially due to the effect of changing wave conditions on wind shear as well as differences in thermal effects over bodies of water. In this study, offshore wind data from the South Atlantic Bight region is used to estimate the offshore wind shear conditions in this area. Data sets include collocated 10 m and 50 m meteorological data as well as wave data, all taken over a three and a half year time period. Offshore wind shear assessments from other studies are analyzed and compared to the current study as well.

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