• 한국태양에너지학회 논문집
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• 제27권1호
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• pp.1-9
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• 2007

This paper reveals both the operational situation and the cause of the error occurred in wind turbine generator system of Hangwon wind farm in Jeju island. The four wind turbines were selected for this work, and the monitored period was for six months. Wind resource in the wind farm was analyzed, and the estimated energy production was compared with the actual energy production. As a result, with a decrease of system error, the estimated energy production was in good agreement with the actual energy production. The errors occurring in components such as gearbox and hydraulic motor affected the Availability of the wind turbine. Also, poor external conditions such as a strong wind, lightning and gust caused a standstill of wind turbines.

• 한국수소및신에너지학회논문집
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• 제24권6호
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• pp.558-565
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• 2013

This paper presents how to determine AEP(Annual Energy Production) by a small wind turbine in DuckjeokDo island. Evaluation of AEP is introduced to make a self-contained island including renewable energy sources of wind, solar, and tidal energy. To determine the AEP in DuckjeokDo island, a local wind data is analyzed using the annual wind data from Korea Institute of Energy Research firstly. After the wind data is separated in 12-direction, a mean wind speed at each direction is determined. And then, a small wind turbine power curve is selected by introducing the capacity of a small wind turbine and the energy production of the wind turbine according to each wind direction. Finally, total annual wind energy production for each small wind turbine can be evaluated using the local wind density and local energy production considering a mechanical energy loss. Throughout the analytic study, it is found that the AEP of DuckjeokDo island is about 2.02MWh/y and 3.47MWh/y per a 1kW small wind turbine installed at the altitude of 10 m and 21m, respectively.

• 한국태양에너지학회 논문집
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• 제33권6호
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• pp.32-38
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• 2013

In an efforts to encourage renewable energy deployment, the government has initiated so called 1 million green homes program but the accumulated installation capacity of small wind turbine has been about 70kW. It can be explained in several ways such that current subsidy program does not meet public expectations, economic feasibility of wind energy is in doubt or acoustic emission is significant etc. The author investigated annual energy production of Skystream 3.7 wind turbine using measured power curve and wind resource data. The measured power curve of the small wind turbine was obtained through power performance tests at Wol-Ryoung test site. AEP(Annual Energy Production) and CF(Capacity Factor) were evaluated at selected locations with the measured power curve.

• 플랜트 저널
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• 제12권3호
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• pp.32-38
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• 2016

본 연구에서는 해안 복잡지형에 2열 배치된 풍력단지 운영사례를 분석하여 후류에 의한 풍력발전기의 영향을 분석하여 풍속의 변화 그리고 난류강도의 변화를 분석하였다. 주풍향 대비 인접 풍력발전기와 이격거리가 날개직경(rotor diameter,RD) 90m 기준 4RD인 경우 후류에 의해 풍속감소 및 난류강도의 증가로 인한 발전량의 감소를 확인 할 수 있었다. 특히 후류에의한 급격한 순간풍향의 변화시, 풍력발전기의 불시정지로 발전량이 현저히 감소되었고 이를 보완하기 위하여 요브레이크를 설비를 보강 함으로써 불시정지 빈도를 감소하고 발전량을 제고하는데 효과가 있음을 확인할 수 있었다.

• 한국태양에너지학회 논문집
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• 제24권3호
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• pp.1-7
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• 2004

Using WAsP, which is PC-program for the vertical and horizontal extrapolation of wind data, annual energy production as well as wind energy density has been predicted for Haengwon wind farm in Jeju island. The predicted results were compared with real data derived from wind turbines in Haengwon wind farm. As the results, in order to produce more electric power, new wind turbines should be located along coastal line, which has comparatively high wind energy density. Also, the roughness length should be inputted to the Map Editor program for better agreement with real annual energy production.

• 한국지구과학회지
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• 제34권7호
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• pp.633-644
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• 2013

풍력 발전단지의 효율성 평가를 위하여 풍력-기상자원지도로부터 이론적 잠재량을 계산하고 행원 발전단지 발전량과 비교하였다. 풍력-기상자원지도는 기상청 국립기상연구소에서 개발된 1 km 해상도 자료를 이용하였다. 풍력-기상자원지도와 AWS(구좌) 풍속의 비교 검증 결과는 풍속이 과대 모의되었으며, 행원 발전단지 풍속의 일 변동성은 해륙풍의 영향으로 오후에 최대가 되는 일 변동 특성을 보였다. 풍력-기상자원지도로부터 산출된 발전량과 행원 발전단지 발전량의 비율은 24.8%이나, 발전량 빈도수 분포 형태는 유사하였다. 발전량 차이의 원인은 터빈의 기계적 오류, 풍력-기상자원지도 풍속의 과대 모의에 기인한 것으로 생각된다. 본 연구는 향후의 발전단지 효율성 증가를 위한 터빈의 재배치에 기여 할 것이다.

• 한국태양에너지학회 논문집
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• 제36권2호
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• pp.9-17
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• 2016

In order to examine how accurately the wind farm design software, WindPRO and Meteodyn WT, predict annual energy production (AEP), an investigation was carried out for Seongsan wind farm of Jeju Island. The one-year wind data was measured from wind sensors on met masts of Susan and Sumang which are 2.3 km, and 18 km away from Seongsan wind farm, respectively. MERRA (Modern-Era Retrospective Analysis for Research and Applications) reanalysis data was also analyzed for the same period of time. The real AEP data came from SCADA system of Seongsan wind farm, which was compare with AEP data predicted by WindPRO and Meteodyn WT. As a result, AEP predicted by Meteodyn WT was lower than that by WindPRO. The analysis of using wind data from met masts led to the conclusion that AEP prediction by CFD software, Meteodyn WT, is not always more accurate than that by linear program software, WindPRO. However, when MERRA reanalysis data was used, Meteodyn WT predicted AEP more accurately than WindPRO.

• 한국태양에너지학회 논문집
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• 제32권3호
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• pp.33-41
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• 2012

As the wind farms in large scale demand enormous amount of construction cost, minimizing the economic burden is essential and also it is very important to measure the wind resources and forecast annual energy production correctly to judge the economic feasibility of the proposed site by way of installing a Met mast at or nearby the site. Wind resources were measured by installing a 80[m] high Met mast at WangdeungYeo Island to conduct the research incorporated in this paper and offshore wind farm was designed using WindPRO. Wind farm of 100[MW] was designed making use of 3 and 4.5[MW] wind generator at the place selected to compare their annual energy production and capacity factor applying the loss factor of 10[%] and 20[%] respectively to each farm. As a result, 336,599[MWh] was generated by applying 3[MW] wind generator while 358,565 [MWh] was produced by 4.5[MW] wind generator. Difference in the energy production by 3[MW] generator was 33,660 [MWh] according to the loss factor with the difference in its capacity factor by 3.8[%]. On the other hand, 23 units of 4.5 [MW] wind generators showed the difference of annual energy production by 35,857 [MWh] with 4.0[%] capacity factor difference.

• 풍력에너지저널
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• 제9권4호
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• pp.32-39
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• 2018

In this study, the annual power production of a wind farm according to obstacles and wind data was predicted for the Garyeok-do wind farm in the Saemangeum area. The Saemangeum Garyeok-do wind farm was built in December 2014 by the Korea Rural Community Corporation. Currently, two 1.5 MW wind turbines manufactured by Hyundai Heavy Industries are installed and operated. Automatic weather station data from 2015 to 2017 was used as wind data to predict the annual power production of the wind farm for three consecutive years. For prediction, a commercial computational fluid dynamics tool known to be suitable for wind energy prediction in complex terrain was used. Predictions were made for three cases with or without considering obstacles and wind direction errors. The study found that by considering both obstacles and wind direction errors, prediction errors could be substantially reduced. The prediction errors were within 2.5 % or less for all three years.

• 한국태양에너지학회:학술대회논문집
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• 한국태양에너지학회 2008년도 추계학술발표대회 논문집
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• pp.300-305
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• 2008

Using WindPRO that was software for windfarm design developed by EMD from Denmark, wind resources for the western Jeju island were analyzed, and the performance of WindPRO prediction was evaluated in detail. The Hansu site and the Yongdang site that were located in coastal region were selected, and wind data for one year at the two sites were analyzed using WindPRO. As a result, the relative error of the Prediction for annual energy Production and capacity factor was about ${\pm}20%$. For evaluating wind energy more accurately, it is necessary to obtain lots of wind data and real electric power production data from real windfarm.