• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 2005.10a
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• pp.156-163
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• 2005

This study was attempted to provide some fundamental data for safety structrural design of biological production facility. Wind load and snow load, acting on agricultural structures is working more sensitive than any other load. Therefore, wind speed and snow depth according to return periods for design load estimation were calculated by frequency analysis using the weather data(maximum instantaneous wind speed, maximum wind speed, maximum depth of snow cover and fall) of 68 regions in Korea. Equations for estimating maximum instantaneous wind speed with maximum wind speed were developed for all, inland and seaside regions. The results were about the same as the current eqution in general. Design wind speed and snow depth according to return periods were calculated and Local design wind load and snow load depending on return periods were presented together with iso-wind speed and iso-snow depth maps. The calculated design snow depth by maximum depth of snow cover were higher than design snow depth by maximum depth of snow fall. Considering wind speed and snow depth, protected cultivation is very difficult in Ullungdo, Gangwon seaside and contiguity inland regions, and strong structural design is needed in the west-south seaside against wind speed, and structure design of biological production facility in these regions need special consideration.

• 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 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.

• Transactions of the Korean hydrogen and new energy society
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• v.30 no.6
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• pp.614-620
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• 2019

In the present study, the wind characteristics were analyzed according to the time averages to evaluate the performance of small wind turbines required for the development of energy independent village. Measuring data of wind speed were recorded between January 2016 and April 2016 every second. Experimental data is averaged out using 5, 10, 15, 20 and 30 minute time steps. Throughout the experimental data analysis, 5 minutes averaged data is used to analyze the performance of the wind turbine, because it produces a minimum turbulence intensity in wind speed. The measuring power of the wind turbine is less than the designed value due to the unsteady nature wind of sudden changes in magnitude of wind speed and wind angle. Detailed wind conditions are also analysed using two variable Weibull probability density functions.

• Wind and Structures
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• v.26 no.3
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• pp.129-146
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• 2018

Extreme wind speed analysis has been carried out conventionally by assuming the extreme series data is stationary. However, time-varying trends of the extreme wind speed series could be detected at many surface meteorological stations in China. Two main reasons, exposure change and climate change, were provided to explain the temporal trends of daily maximum wind speed and annual maximum wind speed series data, recorded at Hangzhou (China) meteorological station. After making a correction on wind speed series for time varying exposure, it is necessary to perform non-stationary statistical modeling on the corrected extreme wind speed data series in addition to the classical extreme value analysis. The generalized extreme value (GEV) distribution with time-dependent location and scale parameters was selected as a non-stationary model to describe the corrected extreme wind speed series. The obtained non-stationary extreme value models were then used to estimate the non-stationary extreme wind speed quantiles with various mean recurrence intervals (MRIs) considering changing climate, and compared to the corresponding stationary ones with various MRIs for the Hangzhou area in China. The results indicate that the non-stationary property or dependence of extreme wind speed data should be carefully evaluated and reflected in the determination of design wind speeds.

• Journal of Wind Energy
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• v.9 no.4
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• pp.40-46
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• 2018

In this study, a three-dimensional Navier-Stokes simulation around Wido Island was performed to analyze the wake effect behind an island. A 10 m/s wind speed and pressure boundary conditions were assigned for the inflow and outflow boundary conditions, respectively. Wido Island was modeled using GIS data. A prevailing wind from the north-northwest direction was determined based on QuikSCAT satellite data. A computational domain of $40km{\times}20km{\times}5km$ covering Wido Island was applied for numerical analysis. Sixty points were specified to extract the wind speed data. A wind speed profile inside the atmospheric boundary layer was compared with a wind profile using a simple power law. It turns out that the wake effect decreases the mean wind speed by 5% more or less, which corresponds to a 14% decrease in wind energy. Thus, the installation of a meteorological mast or development of a wind farm behind Wido Island is not highly recommended.

• Proceedings of the KIEE Conference
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• 2003.04a
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• pp.331-334
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• 2003

In this paper, on the subject of a speed control wind turbine, the type of wind speed reference decision between conventional MPPT tracking speed control and MPPT with LPF(Low Pass Filter) speed control algorithm are introduced and its performances are compared using a model based on MATLAB Simulink, and to get more realistic output data, the stored wind data as its wind speed input from 30kW wind power system in Buan, Haechang is used.

• International Journal of Advanced Culture Technology
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• v.7 no.4
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• pp.327-333
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• 2019

Wind speed data constitute important weather information for aircrafts flying at low altitudes, such as drones. Currently, the accuracy of low altitude wind predictions is much lower than that of high-altitude wind predictions. Deep neural networks are proposed in this study as a method to improve wind speed forecast information. Deep neural networks mimic the learning process of the interactions among neurons in the brain, and it is used in various fields, such as recognition of image, sound, and texts, image and natural language processing, and pattern recognition in time-series. In this study, the deep neural network model is constructed using the wind prediction values generated by the numerical model as an input to improve the wind speed forecasts. Using the ground wind speed forecast data collected at the Boseong Meteorological Observation Tower, wind speed forecast values obtained by the numerical model are compared with those obtained by the model proposed in this study for the verification of the validity and compatibility of the proposed model.

• Journal of Information Processing Systems
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• v.14 no.6
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• pp.1385-1397
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• 2018

There are many factors that affect the wind speed. In addition, the randomness of wind speed also leads to low prediction accuracy for wind speed. According to this situation, this paper constructs the short-time forecasting model based on the least squares support vector machines (LSSVM) to forecast the wind speed. The basis of the model used in this paper is support vector regression (SVR), which is used to calculate the regression relationships between the historical data and forecasting data of wind speed. In order to improve the forecast precision, historical data is clustered by cluster analysis so that the historical data whose changing trend is similar with the forecasting data can be filtered out. The filtered historical data is used as the training samples for SVR and the parameters would be optimized by particle swarm optimization (PSO). The forecasting model is tested by actual data and the forecast precision is more accurate than the industry standards. The results prove the feasibility and reliability of the model.

• Atmosphere
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• v.21 no.4
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• pp.439-446
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• 2011

NCEP reanalysis data were analyzed in order to provide distribution of global wind resource and wind speed in the surface layer for the years 2000-2009. Wind speed at 10 m above ground level (AGL) was converted to wind speed at 80 m above the ground level using the power law. The global average 80 m wind speed shows a maximum value of $13ms^{-1}$ at the storm track region. High wind speed over the land exists in Tibet, Mongolia, Central North America, South Africa, Australia, and Argentina. Wind speed over the ocean increased with a large value in the South China Sea, Southeast Asia, East Sea of the Korea. Sea surface wind in Western Europe and Scandinavia are suitable for wind farm with a value of $7-8ms^{-1}$. Areas with great potential for wind farm are also found in Eastern and Western coastal region of North America. Sea surface wind in Southern Hemisphere shows larger values in the high latitude of South America, South Africa and Australia. The distribution of low-resolution reanalysis data represents general potential areas for wind power and can be used to provide information for high-resolution wind resource mapping.