• Wind and Structures
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• v.31 no.4
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• pp.363-371
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• 2020

Proper understanding of offshore wind speed variability is of essential importance in practice, which provides useful information to a wide range of coastal and marine activities. In this paper, long-term wind speed data recorded at various offshore stations are analyzed in the framework of fractal dimension analysis. Fractal analysis is a well-established data analysis tool, which is particularly suitable to determine the complexity in time series from a quantitative point of view. The fractal dimension is estimated using the conventional box-counting method. The results suggest that the wind speed data are generally fractals, which are likely to exhibit a persistent nature. The mean fractal dimension varies from 1.31 at an offshore weather station to 1.43 at an urban station, which is mainly associated with surface roughness condition. Monthly variability of fractal dimension at offshore stations is well-defined, which often possess larger values during hotter months and lower values during winter. This is partly attributed to the effect of thermal instability. In addition, with an increase in measurement interval, the mean and minimum fractal dimension decrease, whereas the maximum and coefficient of variation increase in parallel.

• Journal of Korean Society for Geospatial Information Science
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• v.18 no.1
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• pp.31-39
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• 2010

After oil shock, the importance of renewable energy has emerged and it came to the fore again as Korean government declared the policy on low-carbon green growth. Among various renewable energies, it is generally accepted that wind power is the most practical alternative. In this paper we showed the process of producing wind speed map from Gangwon Regional Meteorological Administration's 2008 data. We mapped monthly average and maximum wind speed and compared several interpolation methods applied to the weather data. This wind speed map, which reflects Gangwon's topographical and climatic regional characteristics, is expected to be a good tool for wind farm location analysis.

• Journal of the Korean Solar Energy Society
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• v.32 no.2
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• pp.95-104
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• 2012

This study purposed to predict wind energy for small size wind power generators at 50m above the ground in each area using mean wind speed data for 10 minutes collected from 2001 to 2011 by meteorological data in large cities having over 60% of 15 story (50m) or higher apartments including Seoul, Daejeon, Gwangju and Daegu representing the inland region, and Busan, Incheon and Ulsan representing the coastal region. In the results of analysis, we confirmed close agree ment between observatory weather data and probability density distribution obtained using Weibull's parameters, and this suggests that Weibull's parameter is applicable to the estimation of wind energy. Hourly output energy using the mean wind speed for 10 minutes and output energy obtained from Weibull's parameter showed an error less than 5%, and thus it was found that wind energy can be evaluated using Weibull's modulus.

• 한국신재생에너지학회:학술대회논문집
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• 2009.11a
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• pp.456-459
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• 2009

we studied a data acquisition and control system of a wind turbine for measuring and controlling a voltage fluctuations of a wind turbine system. The wind turbine system is installed out control area. So, it is so important for supervising to wind turbine of a maintenance, wind speed, optical resources wind turbine output, wind speed, wind direction, over voltage of a generator. This system can be supplied a data of over voltage, under voltage, voltage fluctuations of a wind turbine for controlling an EMS : Energy Management System or a SCADA : Supervision Control and Data Acquisition at a constitute of a wind farm. The of voltage fluctuation system of a wind turbine is improving an electric power supply power quality of a distribution line and unspecified individuals of used wind turbine.

• New & Renewable Energy
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• v.18 no.2
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• pp.18-25
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• 2022

In this study, we propose a wind power generation prediction system that applies machine learning and data mining to predict wind power generation. This system increases the utilization rate of new and renewable energy sources. For time-series data, the data set was established by measuring wind speed, wind generation, and environmental factors influencing the wind speed. The data set was pre-processed so that it could be applied appropriately to the model. The prediction system applied the CNN (Convolutional Neural Network) to the data mining process and then used the LSTM (Long Short-Term Memory) to learn and make predictions. The preciseness of the proposed system is verified by comparing the prediction data with the actual data, according to the presence or absence of data mining in the model of the prediction system.

• Journal of the Korean Solar Energy Society
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• v.36 no.1
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• pp.49-61
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• 2016

The power production using hub height wind speed tends to be overestimated than actual power production. It is because the hub height wind speed cannot represent vertical wind shear and blade tip loss that aerodynamics characteristic on the wind turbine. The commercial CFD model WindSim is used to compare and analyze each power production. A classification of atmospheric stability is accomplished by Monin-Obukhov length. The concentric wind speed constantly represents low value than horizontal equivalent wind speed or hub height wind speed, and also relevant to power production. The difference between hub height wind speed and concentric equivalent wind speed is higher in nighttime than daytime. Under the strongly convective state, power production is lower than under the stable state, especially using the concentric equivalent wind speed. Using the concentric equivalent wind speed considering vertical wind shear and blade tip loss is well estimated to decide suitable area for constructing wind farm.

• 한국태양에너지학회:학술대회논문집
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• 2008.11a
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• pp.288-293
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• 2008

A predictive model of wind speed in the wind farm has very important meanings. This paper presents an estimation model of wind speed based on time series analysis using the observed wind data at Hangyeong Wind Farm in Jeju island, and verification of the predictive model. In case of Hangyeong Wind Farm and Haengwon Wind Farm, The ARIMA(Autoregressive Integrated Moving Average) predictive model was appropriate, and the wind speed estimation model was developed by means of parametric estimation using Maximum likelihood Estimation.

• Journal of the Korean Solar Energy Society
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• v.30 no.6
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• pp.66-72
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• 2010

In this study, we analyzed the characteristics of wind speed and wind direction at different locations in Jeju area using past 10 years observed data and used them in our wind power forecasting model. Generally the strongest hourly wind speeds were observed during daytime(13KST~15KST) whilst the strongest monthly wind speeds were measured during January and February. The analysis with regards to the available wind speeds for power generation gave percentages of 83%, 67%, 65% and 59% of wind speeds over 4m/s for the locations Gosan, Sungsan, Jeju site and Seogwipo site, respectively. Consequently the most favorable periods for power generation in Jeju area are in the winter season and generally during daytime. The predicted wind speed from the forecast model was in average lower(0.7m/s) than the observed wind speed and the correlation coefficient was decreasing with longer prediction times(0.84 for 1h, 0.77 for 12h, 0.72 for 24h and 0.67 for 48h). For the 12hour prediction horizon prediction errors were about 22~23%, increased gradually up to 25~29% for 48 hours predictions.

• Wind and Structures
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• v.18 no.2
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• pp.195-213
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• 2014

Recently, the horizontal axis rotor performance optimizer (HARP_Opt) tool was developed in the National Renewable Energy Laboratory, USA. This innovative tool is becoming more popular in the wind turbine industry and in the field of academic research. HARP_Optwas developed on the basis of two fundamental modules, namely, WT_Perf, a performance evaluator computer code using the blade element momentum theory; and a genetic algorithm module, which is used as an optimizer. A pattern search algorithm was more recently incorporated to enhance the optimization capability, especially the calculation time and consistency of the solutions. The blade optimization is an aspect that is highly dependent on experience and requires significant consideration on rotor control strategies, wind data, and generator type. In this study, the effects of rotor control strategies including fixed speed and fixed pitch, variable speed and fixed pitch, fixed speed and variable pitch, and variable speed and variable pitch algorithms on optimal blade shapes and rotor performance are investigated using optimized blade designs. The effects of environmental wind data and the objective functions used for optimization are also quantitatively evaluated using the HARP_Opt tool. Performance indices such as annual energy production, thrust, torque, and roof-flap moment forces are compared.

• Wind and Structures
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• v.9 no.6
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• pp.473-492
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• 2006

This paper presents numerical results of pedestrian-level wind environment around the base of a row of tall buildings by CFD. Four configurations of building arrangement are computed including a single square tall building. Computed results of pedestrian-level wind flow patterns and wind speeds are compared to previous wind tunnel measurement data to enable an assessment of CFD predictions. The CFD model uses the finite-volume method with RNG $k-{\varepsilon}$ model for turbulence closure. It is found that the numerical results can reproduce key features of pedestrian-level wind environment such as corner streams around corners of upwind building, sheltered zones behind buildings and channeled high-speed flow through a building gap. However, there are some differences between CFD results and wind tunnel data in the wind speed distribution and locations of highest wind speeds inside the corner streams. In locations of high ground-level wind speeds, CFD values match wind tunnel data within ${\pm}10%$.