• 한국신재생에너지학회:학술대회논문집
• /
• 2010.11a
• /
• pp.190.1-190.1
• /
• 2010

풍력자원평가를 위해 풍력단지 개발대상지의 국지풍황 대표지점에 설치하는 풍황탑(met-tower 또는 풍황마스트; met-mast)은 모노폴(monopole), 삼각단면 트러스 또는 사각단면 트러스 구조를 갖는다. 풍향계 및 풍속계는 이러한 지지구조물에 의한 풍속의 교란 또는 차폐영향을 최소화하기 위하여 긴 붐(boom)의 끝단에 설치되지만 계측기가 풍황탑의 직후방 후류영역에 놓이게 될 경우 차폐영향을 완전히 피하기는 어렵다. 저자들의 선행연구에 따르면 풍황탑 차폐영향은 평균풍력밀도의 경우 2.5% 이상의 오차를 유발할 수 있으므로 풍력자원평가 시 필히 고려되어야 할 불확도 요인인 것이다. 이에 한국에너지기술연구원에서는 풍황탑 주위의 대기유동장을 전산유동해석을 이용하여 차폐영향의 정도를 정량적으로 수치모사함으로써 이를 보정하는 기술을 개발한 바 있다(현재 특허심사 중). KIER-$ShadeFree^{TM}$는 이 특허기술을 프로그램화 한 것으로, 시범적으로 다수의 풍황탑 풍력자원 측정자료에 적용하여 상당한 보정효과에 의한 풍력자원평가의 정확도 향상효과를 볼 수 있었다.

• 한국신재생에너지학회:학술대회논문집
• /
• 2011.11a
• /
• pp.35.1-35.1
• /
• 2011

When the wind speed is measured by the met-mast sensor it is distorted due to the shadow effect of tower. In this paper the tower shadow effect is analyzed by a computational fluid dynamics code. First three dimensional modeling and flow analysis of the met-mast system were performed. The results were compared with the available experimental wind-tunnel test data to confirm the validity of the meshes and turbulence model. Two-dimensional model was then developed based on the three-dimensional works and experimental data. 2D analysis for various Reynolds numbers and turbulence strengths were then performed to establish the tower shadow effect database, which can be utilized as correction factors for the measured wind energy.

• Journal of Wind Energy
• /
• v.4 no.2
• /
• pp.34-39
• /
• 2013

The wind resource assessment for measured wind data over 1 year by using the meteorological mast should be a prerequisite for business feasibility of the wind farm development. Even though the direction of boom mounting the wind vane and anemometer is carefully engineered to escape the interference of wakes generated from the met-mast structures, the shadow effect is not completely avoided due to seasonal winds in the Korean Peninsula. The shadow effect should be properly calibrated because it is able to distort the wind resources. In this study a calibration method is introduced for the measured wind data at Julpo in Jeonbuk Province. Each sectoral terrain conditions along the selected wind direction nearby the met-mast is investigated, and the distorted wind data due to shadow effects can be calibrated effectively. The correction factor is adopted for quantitative calibration by carrying out the WindSim analysis.

• Journal of the Korean Solar Energy Society
• /
• v.33 no.1
• /
• pp.1-6
• /
• 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%.

• 한국신재생에너지학회:학술대회논문집
• /
• 2010.06a
• /
• pp.185.1-185.1
• /
• 2010

원격탐사(remote sensing)란 관측 대상과의 접촉 없이 멀리서 정보를 얻어내는 기술을 말한다. 기상관측분야에는 이미 소다(SODAR) 장비가 폭넓게 사용되거 왔으나 최근 풍력자원평가(wind resource assessment)를 위한 풍황측정에 SODAR와 더불어 라이다(LIDAR)가 적극적으로 활용되기 시작하고 있다. 참고로 SODAR(SOnic Detection And Ranging)는 수직 및 동서 남북 방향으로 음파를 발생시키고 대기유동에 의해 산란 반사된 에코를 수신하여 진동수 변화와 반사에코 강도를 측정하여 각 방향의 에코자료를 벡터 합성함으로써 풍향 및 풍속을 산출하는 원리이다. 반면 LIDAR(Light Detection And Ranging)는 비교적 최근에 풍황측정 용도로 개발된 레이저 탐지에 바탕을 둔 원거리 센서로, 공기입자(먼지, 수증기, 구름, 안개, 오염물질 등)에 의해 산란된 레이저 발산의 도플러 쉬프트(Doppler shift)를 이용하여 풍향 및 풍속을 측정하는 원격탐사 장비이다. 풍력자원평가 측면에서 라이다는 그 정확도가 IEC61400-12에 의거한 풍황탑(met-mast) 측정자료 다수와의 비교검증 실측평가(Albers et al., 2009)를 통하여 입증된 바 있다. 한편 한국에너지기술연구원에서 운용 중인 라이다 시스템은 그림 1의 우측 그림과 같이 1초에 $360^{\circ}$를 스캔하여 50지점에서 반사되는 레이저를 스펙트럼으로 측정하되 설정된 관측높이에서 풍속은 샘플링 부피(sampling volume)의 평균값으로 정의된다. 그런데 샘플링 부피는 설정된 관측높이로부터 상하 12.5m, 총 25m의 높이구간에서 관측한 스펙트럼의 평균값을 그 중앙지점에서의 풍속으로 환산하는 알고리듬(algorithm)을 채택하고 있다. 따라서 비선형적으로 변화하는 풍속연직분포 관측 시 풍속환산 알고리듬에 의한 측정오차가 개입될 가능성이 존재하는 것이다. 이에 본 연구에서는 라이다에 의한 풍속연직분포 측정 시 샘플링 부피의 구간 평균화 과정에서 발생하는 불확도(uncertainty)를 정량적으로 분석함으로써 라이다에 의한 풍속연직분포 관측의 불확도를 정량평가하고자 한다.

• Journal of the Korean Society of Marine Environment & Safety
• /
• v.18 no.6
• /
• pp.545-550
• /
• 2012

Wind resource assessment is necessary when designing wind farm. To get the assessment, we must use a long term(20 years) observed wind data but it is so hard. so that we usually measured more than a year on the planned site. From the wind data, we can calculate wind energy related with the wind farm site. However, it calculate wind energy to collect the long term data from Met-mast(Meteorology Mast) station on the site since the Met-mast is unstable from strong wind such as Typhoon or storm surge which is Non-periodic. To solve the lack of the long term data of the site, we usually derive new data from the long term observed data of AWS(Automatic Weather Station) around the wind farm area using mathematical interpolation method. The interpolation method is called MCP(Measure-Correlative-Predict). In this study, based on the MCP Regression Model proposed by us, we estimated the wind energy at Handong site using AEP(Annual Energy Production) from Gujwa AWS data in Jeju. The calculated wind energy at Handong was shown a good agreement between the predicted and the measured results based on the linear regression MCP. Short term AEP was about 7,475MW/year. Long term AEP was about 7,205MW/year. it showed an 3.6% of annual prediction different. It represents difference of 271MW in annual energy production. In comparison with 20years, it shows difference of 5,420MW, and this is about 9 months of energy production. From the results, we found that the proposed linear regression MCP method was very reasonable to estimate the wind resource of wind farm.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.30 no.3
• /
• pp.295-303
• /
• 2012

Clarify wind energy productivity depends on three factors: the wind probability density function(PDF), the turbine's power curve, and the air density. The wind PDF gives the probability that a variable will take on the wind speed value. Wind shear refers to the change in wind speed with height above ground. The wind speed tends to increase with the height above ground. also, Wind PDF refers to the change with height above ground. Wind analysts typically use the Weibull distribution to characterize the breadth of the distribution of wind speeds. The Weibull distribution has the two-parameter: the scale factor c and the shape factor k. We can use a linear least squares algorithm(or Ln-least method) and moment method to fit a Weibull distribution to measured wind speed data which data was located same site and different height. In this study, find that the scale factor is related to the average wind speed than the shape factor. and also different types of terrain are characterized by different the scale factor slop with height above ground. The gross turbine power output (before accounting for losses) was caculated the power curve whose corresponding air density is closest to the air density. and air desity was choose two way. one is the pressure of the International Standard Atmosphere up to an elevation, the other is the measured air pressure and temperature to calculate the air density. and then each power output was compared.