• Journal of the Korean Solar Energy Society
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• v.37 no.2
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• pp.1-11
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• 2017

The investigation on reliability of ERA-Interim reanalysis wind data was conducted using wind data from the five met masts measured at inland and coastal areas, Jeju island. Shinchang, Handong, Udo, Susan and Cheongsoo sites were chosen for the met mast location. ERA-Interim reanalysis data at onshore and offshore twenty points over Jeju Island were analyzed for creating Wind Statistics using WindPRO software. Reliability of ERA-Interim reanalysis wind data was assessed by comparing the statistics from the met mast wind data with those predicted at the interest point using the Wind Statistics. The relative errors were calculated for annual average wind speed and annual energy production. In addition, the trend of the error was analyzed with distance from met mast. As a result, ERA-Interim reanalysis wind data was more suitable for offshore wind resource assessment than onshore.

• KIEAE Journal
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• v.8 no.1
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• pp.19-24
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• 2008

The wind-power among the new and renewable energies uses the wind, a limitless, clean and pure energy which is available at any place. It requires low installation cost compared to the generation of other renewable energies, and is easy to operate, and furthermore, can be automated for operation. Korea has been taking a great deal of interest in the development of renewable energy generating equipment, specifically wind power generation as the nation has a nearly total reliance on imported petroleum. A measuring poll 30m high was installed at a location with an altitude of 142m above the sea level in order to measure and analyze the wind power potentiality at H University's Asan Campus, and the wind velocity and wind direction were measured for 1 year. As for the wind power resource of the area adjacent to Asan campus, the Weibull Distribution coefficient was C=2.68, K =1.29 at H30m. Weibull Distribution coefficient was modified on the basis of compensated wind velocity (=3.1m/s) at H 60m, and the energy density was $42W/m^2$. AEP 223,750 KWh was forecast based on the simulation of an 800KW grade wind turbine. It is considered that the wind power generation has to be studied further in the inland zone with low wind velocity to cope with the possible exhaustion of fossil fuel and ensure a sustainable environmental preservation.

• New & Renewable Energy
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• v.7 no.2
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• pp.51-58
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• 2011

Saemankeum is well known for its high speed wind, and it is known that the blueprint of a future city around Saemankeum, including new industrial complex, has been planned. As a result, large-scale offshore wind farm, on the basis of the measurement of wind resource for a long time, can be considered, so that generated electricity can be used to meet the energy demand near the wind farm. Wind speed in Kokunsando of Saemankeum is measured and analyzed with its statistical distribution and wind directions. The probability of wind power resource over Kokunsando of Saemangeum is reviewed with the measured data in one island of Kokunsando. According to measured data, the shape and scale factor of Weibull distribution of wind speed are obtained, and then power density is analyzed as well. Through this study, it is clear that the Saemangeum area has a fluent and abundant wind power source to develop the wind farm in Korea.

• Journal of the Korean Solar Energy Society
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• v.33 no.5
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• pp.1-8
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• 2013

Wind resource data of short-term period has to be corrected a long-term period by using MCP method that Is a statistical method to predict the long-term wind resource at target site data with a reference site data. Because the field measurement for wind assessment is limited to a short period by various constraints. In this study, 2 different MCP methods such as Linear regression and Matrix method were chosen to compare the predictive accuracy between the methods. Finally long-term wind speed, wind power density and capacity factor at the target site for 20 years were estimated for the variability of wind and wind energy. As a result, for 20 years annual average wind speed, Yellow sea off shore wind farm was estimated to have 4.29% for coefficient of variation, CV, and -9.57%~9.53% for range of variation, RV. It was predicted that the annual wind speed at Yellow sea offshore wind farm varied within ${\pm}10%$.

• Journal of Power System Engineering
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• v.21 no.6
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• pp.31-39
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• 2017

In order to examine if met-masts wind data can exchange each other for wind resource assessment, an investigation was carried out in Kimnyeong and Haengwon regions of Jeju Island. The two regions are both simple terrain and 4.31 km away from each other. The one-year wind speed data measured by 70 m-high anemometers of each met-mast of the two regions were analysed in detail. Measure-Correlate-Predict (MCP) method was applied to the two regions using the 10-year Automatic Weather System (AWS) wind data of Gujwa region for creating 10-year Wind Statistics by running WindPRO software. The two 10-year Wind Statistics were applied to the self-met mast point for self prediction of Annual Energy Production (AEP) and Capacity Factor (CF) and the each other's met mast point for mutual prediction of them. As a result, when self-prediction values were reference, relative errors of mutual prediction values were less than 1% for AEP and CF so that met masts wind data under the same condition of this study could exchange each other for estimating accurate wind resource.

• Journal of Korean Society for Geospatial Information Science
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• v.20 no.4
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• pp.29-36
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• 2012

In order to create a wind resource map, we need wind data, contour map and roughness map. Moving Filter technique was applied to Udo of Jeju Island to improve the accuracy and efficiency of creating roughness map based on the Land Cover Map of the Ministry of Environment. The Land Cover Map was simplified using moving filtering, and the roughness map was created with this Land Cover Map. The wind resource map was created using this roughness map. Finally, we verified the validity and application of moving filter technique for wind resource map. As a result, the wind map which was created using the roughness map with moving filtering showed bias values which were all negative. It means the wind map is underestimated to values of wind energy and RMSE values were also from 0.0237m/s to 0.0253m/s at 50m height. In other words, estimation of wind resource using image filtering provides reliable results at 80m height typically when the wind turbine is installed. Finally, we found that image filtering technique is very useful tool to make wind resource map.

• 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.

• 한국신재생에너지학회:학술대회논문집
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• 2008.05a
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• pp.324-326
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• 2008

When we urgently need to develop and supply an alternative energy, wind power is growing with much interest because it has relative low cost of power and area of tower. To estimate the wind power resource, it is necessary to make an wind resource map first. On the study of wind resource map in the Korean peninsula, Southern coast was needed to investigate the possibility of developing wind power complex because of good wind resources. In this study, we made a vertical observation to analyze the properties of wind in coastal area. From tethered sonde observation, we knew that synoptic effect had an influence higher in second day than first day. This means local wind circulation is generated on first day but not second day. The local wind made vertical wind shear strong in first day. Also, there was large difference of wind speed between layers at night time by analysis of SODAR observation.

• Journal of Wind Energy
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• v.4 no.1
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• pp.39-45
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• 2013

Massive offshore wind projects of have recently been driven in full gear on the Western Offshore of Korea including the 2.5 GW West-Southern Offshore Wind Project of the Ministry of Trade, Industry and Energy, and the 5 GW Offshore Wind Project of the Jeollanamdo Provincial Government. On this timely occasion, this study performed a general wind resource assessment on the Western Offshore by using the MERRA reanalysis data of temporal-spatial resolution and accuracy greatly improved comparing to conventional reanalysis data. It is hard to consider that wind resources on the Western Sea are excellent, since analysis results indicated the average wind speed of 6.29 ± 0.39 m/s at 50 m above sea level, and average wind power density of 307 ± 53 W/m². Therefore, it is considered that activities shall be performed for guarantee economic profits from factor other than wind resources when developing an offshore wind project on the Western Offshore.

• New & Renewable Energy
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• v.9 no.3
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• pp.14-19
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• 2013

Comparative evaluation of offshore wind retreival software, which use the satellite images taken by Synthetic Aperture Radar sensor; SARTools of CLS-SOPRONO, France and SpaceEye of London Research and Development Corporation, Canada is carried out. For a reference satellite image, ENVISAT ASAR imagery of Jeollanam-do Wan-do area when the winter-time northwestern wind prevails is processed by CMOD_IFR2, CMOD4, CMOD5 algorithms. Wind speed difference and its relative ratio are calculated to evaluate uncertainty of software selection.