• Solar Energy
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• v.6 no.1
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• pp.37-46
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• 1986

This paper deals with analytical methods for estimating the optimal design parameters of wind turbine from power output curve. Asmussen [5] has been used a linearized power output curve for an analytical expression for the specific output of wind turbine generators, but a nonlinear power output curve is developed to determine the design parameters of optimal wind turbine in this study that has maximum specific output and minimum swept area. Thus, the design results of this research will yield reliabilities in construction of wind turbine system and detailed results are presented for several district in Korea. Although the results presented pertain to a wind turbine system without storage, the design approach is equally applicable to system with storage.

• Journal of the Korean Solar Energy Society
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• v.30 no.6
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• pp.10-15
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• 2010

The installed capacity of wind turbines in KOREA are growing and enlarging by the central government's support program. Thus, the importance of power performance verification and its uncertainty analysis are recognizing rapidly. This paper described the power testing results of a 3MW wind turbine and analysed an uncertainty level of measurements. The measured power curves are very closely coincide with the calculated one and the annual power production under the given Rayleigh wind speed distribution are estimated with the 3.6~12.7% of uncertainty but, in the dominant wind speed region as 7~8m/s, the uncertainty are stably decreased to 6.3~5.3%.

• 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.31 no.5
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• pp.60-66
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• 2011

Selecting optimal wind turbine generators for wind farm sites in the capacity factor point of view is performed in this study. A program to determine the best wind turbine generator for the maximum capacity factor for a site was developed. The program uses both the wind characteristics of the site of interest and the power curves of the wind turbines. The program developed was applied to find out optimal wind turbine generators of three different sites in complex terrain and successfully yielded the best site dependent wind turbine generators. It was also used to determine the best wind turbine generator of the wind farm currently operating in Korea and proved its usefulness. The program and methodology developed in this study considered to be very useful at the initial design stage of the wind farm to determine the best wind turbine generators for the site of interest.

• 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 ${\pm}10%$.

• Bulletin of the Korean Space Science Society
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• 2008.04a
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• pp.24.2-24.2
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• 2008

• Journal of the Korean Solar Energy Society
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• v.33 no.5
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• pp.60-68
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• 2013

This paper presents the characteristics of lightning over established and scheduled wind farms of Jeju island as well as over specific range of entire Jeju Island. The lightning data for 5 years from 2008 to 2012 was obtained from IMPACT ESP which detects lightning. Lightning frequency, lightning strength and regional lightning events were analyzed in detail, and then the lightning maps of Jeju Island were created. The evaluation of lightning rate was made for all the wind farms of this study. Damage to wind turbines by lightning was found in the existing wind farms. As a result, the eastern part of Jeju Island had more lightning frequency than the western part of the Island. Also, the evaluation of lightning rate was good for all established and scheduled wind farms of Jeju Island. Hankyung is the best place for lightning safety, while precaution should be taken against lightning damage in Kimnyung. Lightning damage to wind turbines occurred in Samdal and Haengwon wind farms, which had the first and the second highest lightning rate of the five existing wind farms.

• Journal of the Korean Solar Energy Society
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• v.32 no.1
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• pp.1-7
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• 2012

Predictions of wind speed for six different near-shore sites were made using the NCAR (National Center for Atmospheric Research) wind data. The distances between the NCAR sites and prediction sites were varied between 40km and 150km. A well-known wind energy prediction program, WindPRO, was used. The prediction results were compared with the measured data from the AWS(Automated Weather Stations). Although the NCAR wind data were located far away from the AWS sites, the prediction errors were within 9% for all the cases. In terms of sector-wise wind energy distributions, the predictions were fairly close to the measurements, and the error in predicting main wind direction was less than $30^{\circ}$. This proves that the NCAR wind data are very useful in roughly estimating wind energy in offshore or near-shore sites where offshore wind farm might be constructed in Korea.

• Journal of the Korean Solar Energy Society
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• v.33 no.4
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• pp.51-58
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• 2013

A study on power performance testing of a wind turbine which has no met-mast at a distance of 2~4 rotor diameter was carried out using the Nacelle Transfer Function, NTF, according to IEC 61400-12-2. The wind data for this study was measured at HanKyoung wind farm of Jeju Island. The NTF was modeled using the correlation between wind speeds from the met-mast and from the wind turbine nacelle within 2~4 rotor diameter from the met-mast. The NTF was verified by the comparison of estimated Annual Energy Productions, AEPs, and binned power curves. The Nacelle Power Curve, NPC, was derived from the nacelle wind speed data corrected by NTF. The NPC of wind turbine under test and the power curve offered by the turbine manufacturer were compared to check whether the wind turbine is properly generating electricity. Overall the NPC was in good agreement with the manufacturer's power curve. The result showed power performance testing for a wind turbine which has no met-mast at a distance of 2~4 rotor diameter was successfully carried out in compliance with IEC 61400-12-2.

• Journal of Navigation and Port Research
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• v.37 no.1
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• pp.79-84
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• 2013

For the diversity of energy supply system and the improvement in the habitants' living environments of the islands, it is possible and necessary to use the natural energy as resources of the electric power generation system. In this study, the characteristics of wind and solar radiation on 4 islands offshore Namhae-Tongyoung of Korea were measured for one year from November 2010 to October 2011 and analyzed in relation to energy resources survey. As a result of measurement and analysis, the respective wind rose diagrams of 4 islands were made, and showed that the frequencies of wind directions were quite different from among the islands. The Rayleigh probability distribution of wind velocity showed that the wind speeds of KR and SS were mainly 2~5m/s, and the respective quantities of electric power generation of 4 islands were shown to be different. The variation of solar radiations and potential quantity of those uses were measured to be similar to each other among 4 islands.