• Journal of Environmental Science International
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• v.22 no.6
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• pp.717-721
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• 2013

In order to analyze the effect of environmental protection on wind energy dissemination quantitatively, the onshore wind resource potential is estimated using Environmental Conservation Value Assessment Map and Korea Wind Resource Map. The onshore wind resource potential of 11 GW is calculated if wind farm development is prohibited within Class 1 area of Environmental Conservation Value Assessment plus 500 m buffer area. Therefore it is worried that environmental protection would be a practical barrier of accomplishing 4,155 thousand TOE target of wind energy dissemination until 2030.

• Journal of Wind Energy
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• v.9 no.4
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• pp.65-71
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• 2018

To establish an offshore wind turbine test site, a wind resource assessment of the candidate site is required as a preliminary procedure. The wind resource assessment must be performed with at least one year of wind data. If the assessment is performed with short-term wind data, the results cannot validate the wind conditions of the candidate site. This study performs wind resource assessment of Kokunsangun-do to investigate the wind conditions of the candidate site. The wind data is measured by the Automatic Weather System (AWS) of the Korea Meteorological Administration, located at Maldo. The data is for five years, measured from 2013 to 2017. Measured wind data is statistically processed with a 10-minute average scheme to find out the dominant wind direction and wind power density, with yearly wind speed distribution (Weibull-based). This study contributes to build a database of wind energy resources around Maldo. Also, the results of this study could be used for the establishment of an offshore wind turbine test site.

• Journal of the Korean Solar Energy Society
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• v.31 no.2
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• pp.63-71
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• 2011

The remote-sensng campaign was performed at the Pohang Accelerator Laboratory where is located in a basin 6km inland from Yeongil Bay. The campaign aimed uncertainty assessment of Remtech PA0 SODAR through a mutual comparison with WindCube LIDAR, the remote-sensing equipment for wind resource assessment. The joint observation was carried out by changing the setup for measurement heights three times over two months. The LIDAR measurement was assumed as the reference and the uncertainty of SODAR measurement was quantitatively assessed. Compared with LIDAR, the data availability of SODAR was about half. The wind speed measurement was fitted to a slope of 0.94 and $R^2$ of 0.79 to the LIDAR measurement. However, the relative standard deviation was about 17% under 150m above ground level. Therefore, the Remtech PA0 SODAR is judged to be unsuitable for the evaluation of wind resource assessment and wind turbine performance test, which require accuracy of measurement.

• 한국신재생에너지학회:학술대회논문집
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• 2008.05a
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• pp.319-320
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• 2008

This paper introduces remote-sensing data which can be practically applied for offshore wind resource assessment. Development of offshore wind energy is inevitable for Korea to achieve the national dissemination target of renewable energy, i.e., 5% uptil 2010. However, the only available offshore in-situ measurement, marine buoy data would not represent areal wind characteristics. Consequently, remote-sensing technology has been started to apply to offshore wind resource assessment and is actively developing. Among them, NCAR/NCEP reanalysis dataset, QuikSCAT blended dataset, and offshore wind retrieval from SAR imagery are briefly summarized in this paper.

• Journal of Power System Engineering
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• v.20 no.4
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• pp.44-51
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• 2016

An investigation on reliability of reanalysis wind data was conducted using the met mast wind data at four coastal regions, Jeju Island. Shinchang, Handong, Udo and Gangjeong sites were chosen for the met mast sites, and ERA-Interim and MERRA reanalysis data at two points on the sea around Jeju Island were analyzed for creating Wind Statistics of WindPRO software. Reliability of reanalysis wind data was assessed by comparing the statistics from the met mast wind data with those from Wind Statistics of WindPRO software. The relative error was calculated for annual average wind speed, wind power density and annual energy production. In addition, Weibull wind speed distribution and monthly energy production were analyzed in detail. As a result, ERA-Interim reanalysis data was more suitable for wind resource assessment than MERRA reanalysis data.

• Journal of the Korean Solar Energy Society
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• v.25 no.3
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• pp.53-60
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• 2005

Energy resource is very important to the survival in the polar region. Wind energy, which is one of renewable energy, can guarantee the clean, inexhaustible, natural resource in the polar region. It is essential to assess the wind energy resource of the site where wind turbines will be installed. In the present study, the wind characteristics of the antarctic King Sejong station are analysed from its meteorological observation data. The wind resource of the Sejong station is also simulated and predicted using WAsP program. From the results, the Sejong station has very excellent wind resource and the site where small wind turbines will be installed is determined.

• 한국신재생에너지학회:학술대회논문집
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• 2007.06a
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• pp.420-422
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• 2007

In order to support wind power development in Jejudo, the island of winds, the wind resource database KIER-WindJeju has been established by meteor-statistical analysis on met-mast measurements of KIER. The analysis includes tower shading, exposure category, wind profile exponent for wind speed extrapolation to hub height of wind turbine, and correlation matrix between neighboring sites to assist choice of appropriate reference site for long-term correlation. KIER-WindJeju will be provided as an add-on of Google $Earth^{TM}$ and will be used as a guideline of future wind resource assessment in Jejudo.

• Wind and Structures
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• v.20 no.1
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• pp.59-74
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• 2015

A coupled model system for Wind Resource Assessment (WRA) was studied. Using a mesoscale meteorological model, the Weather Research and Forecasting (WRF) model, global-scale data were downscaled to the inner nested grid scale (typically a few kilometers), and then through the coupling Computational Fluid Dynamics (CFD) mode, FLUENT. High-resolution results (50 m in the horizontal direction; 10 m in the vertical direction below 150 m) of the wind speed distribution data and ultimately refined wind farm information, were obtained. The refined WRF/FLUENT system was then applied to assess the wind resource over complex terrain in the northern Poyang Lake region. The results showed that the approach is viable for the assessment of wind energy.

• New & Renewable Energy
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• v.1 no.2 s.2
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• pp.19-25
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• 2005

This study explains ther procedure that should be taken to develp a successful wind park project. It provides a guideline for the activities and studies to be done as a step by step solution. This study follows a chronological flow throughout the whole development Process. This Paper covers technical consideration, assessment of wind energy resource, wind Park siting and energy yield calculation This presented knowledge h3s been mostly gained by the experience from Youngduk wind park project. The further comparison study will be performed between the theoretical prediction and the actual yield of the Youngduk wind park.

• Journal of the Korean Solar Energy Society
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• v.32 no.5
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• pp.94-101
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• 2012

A Dokdo wind resource map has been drawn up for the Green Island Energy Master Plan according to Korea's national vision for 'Low Carbon Green Growth'. The micro-siting software WindSim v5.1,which is based on Computational Flow Analysis, is used with MERRA reanalysis data as synoptic climatology input data, and sensitivity analysis on turbulence model is accompanied. A wind resource assessment has been conducted for the Dokdo wind power dissemination plan, which consists of two 10kW wind turbines to be installed at the Dongdo dock and Dokdo guard building. It is evaluated that the capacity factors at Dongdo dock and Dokdo guard building are about 20% and 30% respectively, and annual and hourly variations of wind power generation have been analyzed, but summertime energy production is predicted to be only 40% of wintertime energy production.