• 한국환경과학회지
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• 제25권2호
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• pp.247-257
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• 2016

To predict annual energy production (AEP) accurately in the wind farm where located in Seongsan, Jeju Island, Equivalent wind speed (EQ) which can consider vertical wind shear well than Hub height wind speed (HB) is calculated. AEP is produced by CFD model WindSim from National wind resource map. EQ shows a tendency to be underestimated about 2.7% (0.21 m/s) than HB. The difference becomes to be large at nighttime when wind shear is large. EQ can be also affected by atmospheric stability so that is classified by wind shear exponent (${\alpha}$). AEP is increased by 11% when atmosphere becomes to be stabilized (${\alpha}$ > 0.2) than it is convective (${\alpha}$ < 0.1). However, it is found that extreme wind shear (${\alpha}$ > 0.3) is hazardous for power generation. This results represent that AEP calculated by EQ can provide improved accuracy to short-term wind power forecast and wind resource assessment.

• 한국지구과학회지
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• 제30권7호
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• pp.845-854
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• 2009

북한의 풍력발전 가능성을 조사하기 위한 첫 단계로 북한 지역에서의 30년 동안의 27개 지점의 지상관측자료 중 풍속과 풍향 자료를 이용하여 이 지역에서의 기후학적 바람 자원을 분석하였다. 바람자원 분석을 위해 풍속의 확률 밀도함수를 Weibull 함수로 가정하여 접근하였다. 지표로부터 50 m 고도에서 연중 평균 풍속이 4.0 m/s 이상인 지역은 대체적으로 개마고원 지역과 황해도 해안 지역이었다. 이 지역들은 비교적 바람 자원이 풍부한 것으로 나타났다. 풍속이 5 m/s 이상을 유지하는 지속시간이 가장 긴 계절은 봄이었고, 짧은 계절은 여름이었다. 관측 지점 중 장진과 양덕이 지속시간이 가장 길고 평균 풍속도 가장 큰 곳이었다.

• 한국환경과학회지
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• 제18권8호
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• pp.899-910
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• 2009

We classified wind sectors according to the wind features in South Korea. In order to get the information of wind speed and wind direction, we used and improved on the atmospheric numerical model. We made use of detailed topographical data such as terrain height data of an interval of 3 seconds and landuse data produced at ministry of environment, Republic of Korea. The result of simulated wind field was improved. We carried out the cluster analysis to classify the wind sectors using the K-means clustering. South Korea was classified as 8 wind sectors to the annual wind field.

• 대한건축학회논문집:구조계
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• 제34권2호
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• pp.29-40
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• 2018

Strength design wind loads for the wind resistance design of structures shall be evaluated by the product of wind loads calculated based on the basic wind speed with 100 years return period and the wind load factor 1.3 specified in the provisions of load combinations in Korean Building Code (KBC) 2016. It may be sure that the wind load factor 1.3 in KBC(2016) had not been determined by probabilistic method or empirical method using meteorological wind speed data in Korea. In this paper, wind load factors were evaluated by probabilistic method and empirical method. The annual maximum 10 minutes mean wind speed data at 69 meteorological stations during past 40 years from 1973 to 2012 were selected for this evaluation. From the comparison of the results of those two method, it can be found that the mean values of wind load factors calculated both probability based method and empirical based method were similar at all meteorological stations. When target level of reliability index is set up 2.5, the mean value of wind load factors for all regions should be presented about 1.35. When target level of reliability index is set up 3.0, wind load factor should be presented about 1.46. By using the relationship between importance factor(conversion factor for return period) and wind load factor, the return periods for strength design were estimated and expected wind speeds of all regions accounting for strength design were proposed. It can be found that return period to estimate wind loads for strength design should be 500 years and 800 years in according to target level of reliability index 2.5 and 3.0, respectively. The 500 years basic wind speed map for strength design was suggested and it can be used with a wind load factor 1.0.

• 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.

• 한국태양에너지학회 논문집
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• 제36권1호
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• pp.11-18
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• 2016

The local wind systems in the Pohang region were categorized into wind sectors. Still, thorough knowledge of wind resource assessment, wind environment analysis, and atmospheric environmental impact assessment was required since the region has outstanding wind resources, it is located on the path of typhoon, and it has large-scale atmospheric pollution sources. To overcome the resolution limitation of meteorological dataset and problems of categorization criteria of the preceding studies, the high-resolution wind resource map of the Korea Institute of Energy Research was used as time-series meteorological data; the 2-step method of determining the clustering coefficient through hierarchical clustering analysis and subsequently categorizing the wind sectors through non-hierarchical K-means clustering analysis was adopted. The similarity of normalized time-series wind vector was proposed as the Euclidean distance. The meteor-statistical characteristics of the mean vector wind distribution and meteorological variables of each wind sector were compared. The comparison confirmed significant differences among wind sectors according to the terrain elevation, mean wind speed, Weibull shape parameter, etc.

• 한국태양에너지학회 논문집
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• 제33권5호
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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.

• 한국환경과학회지
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• 제23권11호
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• pp.1919-1927
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• 2014

In order to decide the location of appropriate onshore wind farm with higher potential wind energy, several decision processes using Geographic Information System (GIS) including Digital Elevation Map (DEM) were proposed and we also estimated the wind resources through the proposed decision process. Decision process consists with three steps. First step is excluding inappropriate location geographically using DEM data including SRTM (Shuttle Radar Topography Mission) terrain data, landslide, land-use, roadway, and forest road data. And the second step of decision process is consideration of the difficulty caused by the natural environmental problem. This step is carried out using ECVAM (Environmental Conservation Value Assessment Map) data. And final step is determination of the most suitable location through the Moving Suitability Identification Method (MSIM) based on the moving potentially estimated wind resources area. Proposed decision process was applied over the Korean Peninsula. Wind resource potential estimated by the first and the second step is cases shows 35.09 GW and 7.17 GW, respectively, and the total evaluated energy from the all proposed step were 0.43 GW and 1.87 GW for the 3 km and 1.5 km geographical grid size, respectively.

• 한국태양에너지학회 논문집
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• 제31권6호
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• pp.8-22
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• 2011

Investigations on modeling methods of a CFD wind resource prediction program, WindSim for a ccurate predictions of wind speeds were performed with the field measurements. Meteorological Masts having heights of 40m and 50m were installed at two different sites in complex terrain. The wind speeds and direction were monitored from sensors installed on the masts and recorded for one year. Modeling parameters of WindSim input variables for accurate predictions of wind speeds were investigated by performing cross predictions of wind speeds at the masts using the measured data. Four parameters that most affect the wind speed prediction in WindSim including the size of a topographical map, cell sizes in x and y direction, height distribution factors, and the roughness lengths were studied to find out more suitable input parameters for better wind speed predictions. The parameters were then applied to WindSim to predict the wind speed of another location in complex terrain in Korea for validation. The predicted annual wind speeds were compared with the averaged measured data for one year from meteorological masts installed for this study, and the errors were within 6.9%. The results of the proposed practical study are believed to be very useful to give guidelines to wind engineers for more accurate prediction results and time-saving in predicting wind speed of complex terrain that will be used to predict annual energy production of a virtual wind farm in complex terrain.

• 한국태양에너지학회 논문집
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• 제35권3호
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• pp.1-8
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• 2015

The MERRA reanalysis data provided online by NASA was applied to predict the monthly energy productions of Donghai Bridge Offshore wind farms in China. WindPRO and WindSim that are commercial software for wind farm design and energy prediction were used. For topography and roughness map, the contour line data from SRTM combined with roughness information were made and used. Predictions were made for 11 months from July, 2010 to May, 2011, and the results were compared with the actual electricity energy production presented in the CDM(Clean Development Mechanism)monitoring report of the wind farm. The results from the prediction programs were close to the actual electricity energy productions and the errors were within 4%.