• 한국신재생에너지학회:학술대회논문집
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• 2005.06a
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• pp.42-46
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• 2005

In spite of fast growing of prediction codes, there is still not negligible uncertainty in their results. This uncertainty affects on the turbine structural design and power production prediction. With the growing size of wind turbine, reducing this uncertainty is becoming one of critical issues for high performance and efficient wind turbine design. In this respect, there are international efforts to evaluate and tune prediction codes of wind turbine. As the reference data for this purpose, field test data is not appropriate because of its uncontrollable wind characteristics and its inherent uncertainty. Wind tunnel can provide controllable wind. For this reason, NREL has done the full scale test of the 10m turbine at NASA-Ames. With this reference data, a blind comparison has been done with participation of 18 organizations with 19 modeling tools. The results were not favorable. In Europe, a similar project is going on. Nine organizations from five countries are participating in the MEXICO project to do full scale wind tunnel tests and calculation with prediction codes. In this study. these two projects were reviewed in respect of wind tunnel test and its contribution. As a conclusion, it is suggested that scale model wind tunnel tests can be a complementary tool to calculation codes which were evaluated worse than expected.

• Journal of Wind Energy
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• v.14 no.3
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• pp.54-60
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• 2023

In order to reveal the levelized cost of energy (LCOE) of Korean and foreign wind turbines, a study was conducted for Korean onshore wind farms. Actual CapEx and OpEx data were obtained from audit reports for 26 onshore wind farms corresponding to 53.87 percent of the total onshore wind farms in Korea in the Data Analysis, Retrieval Transfer (DART) system. In addition, capacity factor (CF) data were calculated from data provided by Statistics Korea. Random numbers were generated from distributions that were fitted by the datasets, which were used as input data to perform a Monte Carlo simulation (MCS). The levelized fixed cost (LFC) and the levelized variable cost (LVC) were calculated from distributions of the CapEx, the OpEx and the CF. As a result, the LCOEs of the analyzed total Korean wind farms, and Korean and foreign wind turbines were 147, 148, and 146 USD/MWh, respectively. The averaged LCOE of Korea was estimated to be 4 USD/MWh lower than that of Japan, while it was much higher than German and global averages.

• Journal of Environmental Science International
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• v.17 no.2
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• pp.195-201
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• 2008

Damage from typhoon disaster can be mitigated by grasping and dealing with the damage promptly for the regions in typhoon track. What is this work, a technique to analyzed dangerousness of typhoon should be presupposed. This study estimated 10 m level wind speed using 700 hPa wind by typhoon, referring to GPS dropwindsonde study of Franklin(2003). For 700 hPa wind, 30 km resolution data of Regional Data Assimilation Prediction System(RDAPS) were used. For roughness length in estimating wind of 10 m level, landuse data of USGS are employed. For 10 m level wind speed of Typhoon Rusa in 2002, we sampled AWS site of $7.4{\sim}30km$ distant from typhoon center and compare them with observational data. The results show that the 10 m level wind speed is the estimation of maximum wind speed which can appear in surface by typhoon and it cannot be compared with general hourly observational data. Wind load on domestic buildings relies on probability distributions of extreme wind speed. Hence, calculated 10 m level wind speed is useful for estimating the damage structure from typhoon.

• 한국신재생에너지학회:학술대회논문집
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• 2009.06a
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• pp.529-531
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• 2009

To construct the wind map for mainland Korea, the well designed atmospheric numerical modeling system was used. Three nest domains were construced with spatial resolutions between $10{\times}10km$ up to the hightest resolution of $1{\times}1km$. Parameterization schemes like MRF(PBL), RRTM(radiation), Grell(cumulus) were chosen since wind data simulated is in better agreement with the observed wind data. High-resolution atmospheric numerical model was applied to simulate the motion of the atmosphere and to produce the wind map around the South Korea. The results of several simulations were improved compare to the past system, because of using the fine geographical data, such as terrain height and land-use data, and the meteorological data assimilation.

• Journal of Wind Energy
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• v.9 no.4
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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.

• 한국신재생에너지학회:학술대회논문집
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• 2010.11a
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• pp.191-191
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• 2010

As many researchers want to predict or assess more about wind condition and wind power generation, CFD(Computational Fluid Dynamics) analysis method is very good way to do predict or assess wind condition and power generation. But CFD analysis is needed much knowledge of aerodynamics and physical fluid theory. In this paper, Auto-Wind CFD analysis program will be introduced. User does not need specific knowledge of CFD or fluid theory. This program just needs topographical data and wind data for initial condition. Then all of process is running automatically without any order of user. And this program gives for user to select and set initial condition for advanced solving CFD. At the last procedure of solving, Auto-Wind program shows analysis of topography and wind condition of target area. Moreover, Auto-Wind can predict wind power generation with calculation in the program. This Auto-Wind analysis program will be good tool for many wind power researchers in real field.

• Wind and Structures
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• v.19 no.3
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• pp.271-294
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• 2014

The impact of artefacts in archived wind observations on the design wind speed obtained by extreme value analysis is demonstrated using case studies. A signpost protocol for detecting candidate artefacts is described and its performance assessed by comparing results against previously validated data. The protocol targets artefacts by exploiting the serial correlation between observations. Additional "sieve" algorithms are proposed to identify types of correctable artefact from their "signature" in the data. In extreme value analysis, artefacts displace valid observations only when they are larger, hence always increase the design wind speed. Care must be taken not identify large valid values as artefacts, since their removal will tend to underestimate the design wind speed.

• Journal of Korean Society of Steel Construction
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• v.22 no.6
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• pp.573-580
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• 2010

In this study, an assessment method that considers the effects of directional wind speeds on buildings or structures that are sensitive to wind is proposed. Also, the basic characteristics of directional wind speeds were assessed by means of local annual maximum wind speeds. From the method of assessment of the characteristics of directional wind speeds, their goodness-of-fit was verified by applying extreme value distribution to the data on annual maximum wind speeds from the Korea Meteorological Administration. To consider the characteristics of directional winds, an assessment method is suggested that divides the directional wind pattern of each directional wind speed into four groups. From the study results, all the data on directional wind speeds based on the Gumbel distribution were examined using data on annual maximum wind speeds from Seoul, Tongyung, and Incheon. Since the Gumbel model of all directional wind speeds has independent probability characteristics that govern the 4 directional wind pattern groups, the application ratio proposed was based on the assessment of these four groups. According to the goodness-of-fit of the data on the annual maximum wind speeds based on the Gumbel distribution, new application ratios were proposed that consider the directional wind speeds in Seoul, Tongyung, and Incheon.

• Smart Structures and Systems
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• v.21 no.5
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• pp.591-600
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• 2018

The probabilistic characterization of wind field characteristics is a significant task for fatigue reliability assessment of long-span railway bridges in wind-prone regions. In consideration of the effect of wind direction, the stochastic properties of wind field should be represented by a bivariate statistical model of wind speed and direction. This paper presents the construction of the bivariate model of wind speed and direction at the site of a railway arch bridge by use of the long-term structural health monitoring (SHM) data. The wind characteristics are derived by analyzing the real-time wind monitoring data, such as the mean wind speed and direction, turbulence intensity, turbulence integral scale, and power spectral density. A sequential quadratic programming (SQP) algorithm-based finite mixture modeling method is proposed to formulate the joint distribution model of wind speed and direction. For the probability density function (PDF) of wind speed, a double-parameter Weibull distribution function is utilized, and a von Mises distribution function is applied to represent the PDF of wind direction. The SQP algorithm with multi-start points is used to estimate the parameters in the bivariate model, namely Weibull-von Mises mixture model. One-year wind monitoring data are selected to validate the effectiveness of the proposed modeling method. The optimal model is jointly evaluated by the Bayesian information criterion (BIC) and coefficient of determination, $R^2$. The obtained results indicate that the proposed SQP algorithm-based finite mixture modeling method can effectively establish the bivariate model of wind speed and direction. The established bivariate model of wind speed and direction will facilitate the wind-induced fatigue reliability assessment of long-span bridges.

• Journal of Environmental Science International
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• v.18 no.8
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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.