• 한국음향학회지
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• 제40권1호
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• pp.73-83
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• 2021

풍력 발전은 미래의 청정 에너지원으로 활용 가치가 높으나, 초저주파 소음을 발생한다. 초저주파 소음은 차폐가 어려우며, 생리적으로 다양한 부정적인 효과를 유발할 수 있다. 본 논문에서는 Wind Turbine Syndrome (WTS)을 유발하는 초저주파수 풍력 소음의 유해성을 개관하고, 풍력 발전기 소음의 측정 및 관리를 위한 기술적인 측면을 고찰하고자 한다.

• 한국태양에너지학회 논문집
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• 제33권1호
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• pp.1-6
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• 2013

It is difficult to avoid measurement errors caused by the shading effect of the meteorological tower, which is used for wind resource assessment according to the IEC Standard. This paper presents a validation of the computational flow analysis results by comparing the results with the wind tunnel experiment conducted for Reynolds numbers in the $10^4$ to $10^5$ range, for the preparation of a database for use in an automatic method of correcting met-tower shading errors. A three-dimensional simulation employing the MP (Modified Production) $k-{\varepsilon}$ turbulence model predicted a wind speed deficit in the wake region according to minimum wind speed ratio, within an MAE (Mean Absolute Error) of 2.4%.

• 풍력에너지저널
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• 제15권1호
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• pp.50-59
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• 2024

Floating LiDAR systems provide significant savings in cost and time compared to the fixed meteorological mast measurement type, and have the advantage of being able to be deployed in various locations due to less restriction on the depth of the installation site. However, to use the wind data collected by a floating LiDAR system commercially, verification procedure is required to ensure that the collected data have sufficient availability. The Carbon Trust OWA roadmap presents guidelines in three stages for the reliability of the wind data collected using a floating LiDAR system. Companies developing wind farms are requesting at least Stage 2 (pre-commercial stage) presented by OWA, and many overseas companies are leading the domestic and overseas markets. In this paper, we introduce the case of OWA Stage 2 certification for the commercial operation of floating LiDAR systems.

• 신재생에너지
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• 제4권4호
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• pp.23-29
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• 2008

Wind farm development project contains high business risks because that a wind farm, which is to be operating for 20 years, has to be designed and assessed only relying on a year or little more in-situ wind data. Accordingly, long-term correction of short-term measurement data is one of most important process in wind resource assessment for project feasibility investigation. This paper shows comparison of general Measure-Correlate-Prediction models and neural network, and presents new method using neural network for increasing prediction accuracy by accommodating multiple reference data. The proposed method would be interim step to complete long-term correction methodology for Korea, complicated Monsoon country where seasonal and diurnal variation of local meteorology is very wide.

• 신재생에너지
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• 제1권1호
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• pp.44-53
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• 2005

To understand general status of the national wind environment and to distinguish potential areas to be developed as a largescale wind farm, a synoptic wind map of the Korean Peninsula is established by processing remote sensing data of the satellite, NASA QuikSCAT which Is deployed for the SeaWinds Project since 1999. According to the validation results obtained by comparing with the measurement data of marine buoys of KMA(Korea Meteorological Administration), the cross-correlation factor Is greatly Improved up to 0.87 by blending the sea-surface dat3 of QuikSCAT with NCEP/NCAR CDAS data. It is found from the established synoptic wind map that the wind speed in winter is prominent temporally and the South Sea shows high energy density up to the wind class 6 spatially. The reason is deduced that the northwest winds through the yellow Sea and the northeast winds through the East Sea derived by the low-pressure developed in Japan are accelerated passing through the Korea Channel and formed high wind energy region in the South Sea; the same trends are confirmed by the statistical analysis of meteorological observation data of KMA.

• 한국태양에너지학회:학술대회논문집
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• 한국태양에너지학회 2009년도 춘계학술발표대회 논문집
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• pp.277-280
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• 2009

Wind monitoring system is an absolutely-required system for assessing a performance and fatigue load of the wind energy generator in an on-shore wind energy experimental research complex. It was implemented for the purpose of monitoring the wind information measured from a meteorological tower at the monitoring house and of utilizing the measured data for the performance assessment, by using the LabVIEW program. Then, by adding the performance assessment-related data acquired from the wind energy generator during the performance assessment and the data recorder for synchronizing the data of meteorological tower, the system was implemented. Because it transmitted the data by converting the output 'RS-232' of data logger which measures the wind condition into CAN protocol, the data error rate was minimized, This paper is intended to explain the developed wind data monitoring system.

• 한국콘텐츠학회:학술대회논문집
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• 한국콘텐츠학회 2006년도 추계 종합학술대회 논문집
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• pp.465-468
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• 2006

대체 에너지의 주요 자원의 하나인 풍력 발전은 그 실용성을 평가하기 위하여 중장기적인 자원 가치에 대한 실사와 검증이 필요하다. 하지만 검증을 위한 계측 장비의 설치가 주로 오지에 이루어지기 때문에 대부분의 검증방식이 에너지 자원에 대한 데이터 수집을 실시간으로 하지 못하고 주기적 방문을 통하여 축척된 데이터를 수집하고 있다. 본 논문에서는 실시간 데이터 및 서비스 제공 시스템을 설계, 구현함으로써 풍력 에너지 실측, 활용의 신뢰성을 확보할 수 있도록 한다. 또한 웹 서비스를 기반으로 구축함으로써 다양한 유형의 단말기에 정보 및 서비스를 효율적으로 제공할 수 있도록 한다.

• 한국항해항만학회지
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• 제37권1호
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• pp.79-84
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• 2013

도서지역에 대한 에너지공급시스템의 다양화를 위해서는 지역 내에 풍부한 자연에너지 활용이 필요하고 이를 통해 도서주민의 생활환경 개선이 가능하다. 이에 본 연구에서는 지역에 특화된 자연에너지를 이용한 전력공급시스템을 제안하기 위한 기초자료 구축을 목적으로 통영-남해 해역에 위치한 4개 섬의 풍력과 일사량을 1년간 (2010년 11월~2011년 10월) 실측하여 발생 특성을 파악하고 활용가능성을 비교 평가하였다. 본 연구의 성과를 정리하면 다음과 같다. 풍향별 발생 빈도수를 이용하여 작성한 바람장미로 부터 4개 섬의 풍향 특성이 크게 다르다는 것을 알았다. Rayleigh 속도확률분포를 이용한 평가에 따르면 KR섬과 SS섬은 2~5m/s의 바람이 많이 불지만, SR섬과 YJ섬은 2m/s이하의 저속 바람의 발생 확률이 매우 높은 것으로 분석되었고, 4개 섬 각각의 풍력발전량에는 큰 차이가 있었다. 4개 섬 모두에서 최대 일일 일사량은 2011년 7월에 발생하였지만, 평균 일일 일사량은 2011년 4월이 가장 컸다. 또한 월별 누적 일사량의 경우 2011년 4월이 가장 많았고, 2010년 12월 가장 적었다.

• 한국태양에너지학회 논문집
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• 제30권4호
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• pp.9-14
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• 2010

The main cause of global warming is carbon dioxide generated from the use of fossil fuels, and active research on the reduction of carbon is in progress to slow down the increasing global warming. Wind turbines generate electricity from kinetic energy of wind and are considered as representative for an energy source that helps to reduce carbon emission. Since the kinetic energy of wind is proportional to the cube of the wind speed, the intensity of wind affects wind farm construction validity the most. Therefore, to organize a wind farm, validity analysis should be conducted first through measurement of the wind resources. To facilitate the approval and permission and reduce installation cost, measuring sensors should be installed at locations below the actual wind turbine hub. Wind conditions change in shape with air density, and air density is most affected by the variable sterrain and surface type. So the magnitude of wind speed depends on the ground altitude. If wind conditions are measured at a location below the wind turbine hub, the wind speed has to be extrapolated to the hub height. This correction of wind speed according to height is done with the Deacon equation used in the statistical analysis of previously observed data. In this study, the optimal Deacon equation parameter was obtained through the analysis of the correction of the wind speed error with the Deacon equation based on the characteristics of terrain.

• 한국환경과학회지
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• 제18권10호
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• pp.1115-1121
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• 2009

The purpose of this study is to estimate the impact of rainfall measurement according to the installation conditions of rain gauges: windbreak, grass mat, installation elevation or obstacle. Rain gauges were installed by the standards of Korea Meteorological Administration(KMA), and the rainfall measurement was conducted daily unit during two years(2007~2008). In conclusion, observed error of rain gauge did not affect whether windbreak was installed or not. If there is the obstacle around rain gauge, average error rate was increased about 3.3%: (2007year-2.49%, 2008year-4.10%). If rain gauge is located in a high place, average error rate was increased about 4.89%. Additionally, the observed error of rain gauge according to the wind speed has a positive correlation with obstacle and installation elevation and has a negative correlation with windbreak and has no affection with grass mat.