• Journal of the Korean Solar Energy Society
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• v.30 no.5
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• pp.44-55
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• 2010

The proposed offshore wind farm projects, i.e., Mooudo offshore, Yeonggwang-Gochang offshore, Saemangeum offshore, Imjado offshore and Gadeokdo-Dadeapo offshore, were compared and analyzed using the Korea National Wind Mapand Wind Farm Suitability Assessment System developed by the Korea Institute of Energy Research. The suitability of the proposed areas was comprehensively assessed using geographic, economic constraints, wave condition and wind resource factors, but the focus of this paper was on the geographic constraints and wave conditions. Imjado had several geographical constraints, despite having a good wind power density, while Saemangeum had a relatively low wave height, shallow water depth, close substation and slow tidal current. It is anticipating that the present comparison and analysis could be used as reference guidelines when selecting and preparing the design of large-scale offshore wind farm in the near future.

• Transactions of the Korean hydrogen and new energy society
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• v.30 no.6
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• pp.614-620
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• 2019

In the present study, the wind characteristics were analyzed according to the time averages to evaluate the performance of small wind turbines required for the development of energy independent village. Measuring data of wind speed were recorded between January 2016 and April 2016 every second. Experimental data is averaged out using 5, 10, 15, 20 and 30 minute time steps. Throughout the experimental data analysis, 5 minutes averaged data is used to analyze the performance of the wind turbine, because it produces a minimum turbulence intensity in wind speed. The measuring power of the wind turbine is less than the designed value due to the unsteady nature wind of sudden changes in magnitude of wind speed and wind angle. Detailed wind conditions are also analysed using two variable Weibull probability density functions.

• Journal of Wind Energy
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• v.14 no.3
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• pp.14-24
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• 2023

The significance of renewable energy has been on the rise, as evidenced by the 3020 renewable energy plan and the 2050 carbon neutrality strategy, which seek to advance a low-carbon economy by implementing a power supply strategy centered around renewable energy sources. This study examines the wind resources on the west coast of South Korea and confirms the potential for wind power generation in the area. Wind speed data was collected from 22 automatic weather system stations and four light house automatic weather system stations provided by the Korea Meteorological Administration to evaluate potential sites for wind farms. Weibull distribution was used to analyze the wind data and calculate wind power density. Annual energy production and capacity factors were estimated for 15-20 MW-class large wind turbines through the height correction of observed wind speeds. These findings offer valuable information for selecting wind power generation sites, predicting economic feasibility, and determining optimal equipment capacity for future wind power generation sites in the region.

• Journal of Korean Society of Rural Planning
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• v.14 no.2
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• pp.25-32
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• 2008

Wind energy, which is one of renewable energy, would be useful resources that can be applied to making energy recycling villages without using fossil fuels. This study analyzed energy potential on wind power considering weather condition in three rural villages and compared with energy consumption surveyed. A wind turbine system in the 5kW class can generate 26.1%, 73.9% and 39.5% of the yearly mean consumption of electric power per house in Makhyun, Boojang and Soso respectively. A 750kW wind turbine system can generate 1.7%, 30.3% and 22.1% of the total amount of electric power consumption in three study villages respectively. Wind power energy density was too low in Makhyun and Soso, so it is determined that the application of wind turbine system is almost impossible. Wind energy potential was generally low in Boojang either, but it is evaluated that there is a little possibility of wind power generation relatively. For practical application of renewable energy to rural green-village planning, assessment of energy potential for the local area should be preceded.

• Journal of the Korean Solar Energy Society
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• v.29 no.2
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• pp.22-30
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• 2009

In order to clarify predictive accuracy for the wind resource predicted by running WindPRO(Ver. 2.5) which is software for wind farm design developed by EMD from Denmark, an investigation was carried out at the northeast region of Jeju island. The Hangwon, Susan and Hoichun sites of Jeju island were selected for this study. The measurement period of wind at the sites was for one year. As a result, when the sites had different energy roses, though the two Wind Statistics made by STATGEN module were used for the prediction, it was difficult to exactly predict the energy rose at a given site. On the other hand, when the two Wind Statistics were used to predict the average wind speed, the wind power density and the annual energy production, the relative error was under ${\pm}20%$ which improved more than that when using only one Wind Statistics.

• 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 ${\pm}10%$.

• 한국태양에너지학회:학술대회논문집
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• 2008.11a
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• pp.81-84
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• 2008

This paper discussed the validity of wind force power generation in consideration of the topographical characteristics of Korea. In order to estimate the exact generation of wind power plants, we analyzed and compared wind resources in mountain areas and plain areas by introducing not only wind velocity, the most important variable, but also wind distribution and wind standard deviation that can reflect the influence of landform sufficiently. According to the results of this study, generation was higher at wind power plants installed in southwestern coastal areas where wind velocity was low than at those installed in mountain areas in Gangwondo where wind velocity was high. This suggests that the shape parameter of wind distribution is low due to the characteristics of mountain areas. and the standard deviation of wind velocity is large due to the effect of mountain winds, and therefore, actual generation is low in mountain areas although wind velocity is high.

• Journal of Wind Energy
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• v.14 no.1
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• pp.37-43
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• 2023

In this study, synthetic time series wind data was generated numerically using a second-order Markov chain. One year of wind data in 2020 measured by the AWS on Wido Island was used to investigate the statistics for measured wind data. Both the transition probability matrix and the cumulative transition probability matrix for annual hourly mean wind speed were obtained through statistical analysis. Probability density distribution along the wind speed and autocorrelation according to time were compared with the first- and the second-order Markov chains with various lengths of time series wind data. Probability density distributions for measured wind data and synthetic wind data using the first- and the second-order Markov chains were also compared to each other. For the case of the second-order Markov chain, some improvement of the autocorrelation was verified. It turns out that the autocorrelation converges to zero according to increasing the wind speed when the data size is sufficiently large. The generation of artificial wind data is expected to be useful as input data for virtual digital twin wind turbines.

• 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 earth science society
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• v.31 no.3
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• pp.225-233
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• 2010

Wind power density distribution over the North Korea territory was investigated by using 30-year wind observations at 27 meteorological stations. The mean annual wind power density over North Korea turned out to be 58.6W/$m^2$, which corresponds to the wind power class of 1. The wind power density shows a seasonal variation, having the highest density in spring and the lowest in summer. In particular, the wind power density in summer is about a half of that in spring. The diurnal variation of the wind power density shows that the highest and lowest densities occur in the afternoon and between 3 and 6 am in local time, respectively. The most potential wind energy generation regions are the Gaema Plateau in the central region, the northeast part of Hamgyeongbuk-do, the south coast of Pyongan-do and the west coast of Hwanghae-do. The mean annual wind power density in Changjin is 151.2W/$m^2$, which is equivalent to the class of 3. In Ryongyon, the annual mean wind power density is 102.4W/$m^2$, which belongs to the class of 2.