• 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.

• KEPCO Journal on Electric Power and Energy
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• v.2 no.4
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• pp.627-630
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• 2016

Recently, the size of wind farms is becoming larger, and the integration of high wind generation resources into power gird is becoming more important. Due to intermittency of wind generating resources, it is an essential to predict power outputs. In this paper, we introduce the basic concept of curvilinear regression, which is one of the method of wind power prediction. The empirical data, wind farm power output in Jeju Island, is considered to verify the proposed prediction model.

• KEPCO Journal on Electric Power and Energy
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• v.6 no.4
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• pp.433-438
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• 2020

Accurate forecasting of wind power is important for grid operation. Wind power has intermittent and nonlinear characteristics, which increases the uncertainty in wind power generation. In order to accurately predict wind power generation with high uncertainty, it is necessary to analyze the factors affecting wind power generation. In this paper, 6 factors out of 11 are selected for more accurate wind power generation forecast. These are wind speed, sine value of wind direction, cosine value of wind direction, local pressure, ground temperature, and history data of wind power generated.

• Journal of Power Electronics
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• v.9 no.1
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• pp.85-92
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• 2009

This paper studies the method to predict voltage variation that can be presented in the case of operating a small-sized wind turbine in grid connection to the isolated small-sized power system. In order to do this, it makes up the simplified simulation model of the existing power plant connected to the isolated system, load, transformer, and wind turbine on the basis of PSCAD/EMTDC and compares them with the operating characteristics of the actual established wind turbine. In particular, it suggests a simplified model formed with equivalent impedance of the power system network including the load to analytically predict voltage variation at the connected point. It also confirms that the voltage variation amount calculated by the suggested method accords well with both simulation and actually measured data. The results can be utilized as a tool to ensure security and reliability in the stage of system design and preliminary investigation of a small-sized grid connected wind turbine.

• Proceedings of the KIEE Conference
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• 2009.07a
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• pp.1112_1113
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• 2009

This paper proposes the method to predict the wind turbine power generation using logic-based fuzzy neural networks. To predict the wind turbine power generation neural networks, logic-based fuzzy neural networks, and fuzzy neural models have been considered. But the model considered in this paper can predict the wind turbine power generation with a less complex structure. The simulation results show the effectiveness of the proposed method.

• 한국신재생에너지학회:학술대회논문집
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• 2006.06a
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• pp.323-324
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• 2006

In the present paper a forecasting system of wind power generation for Walryong Site, Jejudo is presented, which has been developed and evaluated as a first step toward establishing Korea Forecasting Model of Wind Power Generation. The forecasting model, KIER forecaster is constructed based on statistical models and is trained with wind speed data observed at Gosan Weather Station nearby Walryong Si to. Due to short period of measurements at Walryong Site for training statistical model, Gosan wind data were substituted and transplanted to Walryong Site by using Measure-Correlate-Predict technique. Three-hour advanced forecast ins shows good agreement with the measurement at Walryong site with the correlation factor 0.88 and MAE(mean absolute error) 15% under.

• Journal of Power Electronics
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• v.15 no.4
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• pp.1047-1053
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• 2015

A time-domain simulation tool to predict the dynamic power output of wind turbines in an offshore wind farm was developed in this study. A wind turbine model consisting of first or second order transfer functions of various wind turbine elements was combined with the Ainslie's eddy viscosity wake model to construct the simulation tool. The wind turbine model also includes an aerodynamic model that is a look up table of power and thrust coefficients with respect to the tip speed ratio and pitch angle of the wind turbine obtained by a commercial multi-body dynamics simulation tool. The wake model includes algorithms of superposition of multiple wakes and propagation based on Taylor's frozen turbulence assumption. Torque and pitch control algorithms were implemented in the simulation tool to perform max-Cp and power regulation control of the wind turbines. The simulation tool calculates wind speeds in the two-dimensional domain of the wind farm at the hub height of the wind turbines and yields power outputs from individual wind turbines. The NREL 5MW reference wind turbine was targeted as a wind turbine to obtain parameters for the simulation. To validate the simulation tool, a Danish offshore wind farm with 80 wind turbines was modelled and used to predict the power from the wind farm. A comparison of the prediction with the measured values available in literature showed that the results from the simulation program were fairly close to the measured results in literature except when the wind turbines are congruent with the wind direction.

• New & Renewable Energy
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• v.2 no.2 s.6
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• pp.37-43
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• 2006

In this paper, the first forecasting system of wind power generation, KIER Forecaster is presented. KIER Forecaster has been constructed based on statistical models and was trained with wind speed data observed at Gosan Weather Station nearby Walryong Site. Due to short period of measurements at Walryong Site for training the model, Gosan wind data were substituted and transplanted to Walryong Site by using Measure-Correlate-Predict(MCP) technique. The results of One to Three-hour advanced forecasting models are consistent with the measurement at Walryong site. In particular, the multiple regression model by classification of wind speed pattern, which has been developed in this work, shows the best performance comparing with neural network and auto-regressive models.

• 한국신재생에너지학회:학술대회논문집
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• 2009.06a
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• pp.525-528
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• 2009

Power generation of wind turbine which is installed in wind farm should be measured to predict economic feasibility of wind farm. Also electric power company want to verify wind turbine performance which is stated by manufacturer. The International Electrotechnical Commission(IEC) published 61400-12-1 "Power performance measurements of electricity producing wind turbines" for test of wind turbine power performance. In this paper, measurable sector of wind speed is analysed based on IEC 61400-12-1 to verify power curve of wind turbine with various wind turbine in wind farm.

• 한국전산유체공학회:학술대회논문집
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• 2008.03b
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• pp.652-655
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• 2008

Despite of the laminar-turbulent transition region co-exist with fully turbulence region around the leading edge of an airfoil, still lots of researchers apply to fully turbulence models to predict aerodynamic characteristics. It is well known that fully turbulent model such as standard k-${\varepsilon}$ model couldn't predict the complex stall and the separation behavior on an airfoil accurately, it usually leads to over prediction of the aerodynamic characteristics such as lift and drag forces. So, we apply correlation based transition model to predict aerodynamic performance of the NREL (National Renewable Energy Laboratory) Phase IV wind turbine. And also, compare the computed results from transition model with experimental measurement and fully turbulence results. Results are presented for a range of wind speed, for a NREL Phase IV wind turbine rotor. Low speed shaft torque, power, root bending moment, aerodynamic coefficients of 2D airfoil and several flow field figures results included in this study. As a result, the low speed shaft torque predicted by transitional turbulence model is very good agree with the experimental measurement in whole operating conditions but fully turbulent model(k-${\varepsilon}$) over predict the shaft torque after 7m/s. Root bending moment is also good agreement between the prediction and experiments for most of the operating conditions, especially with the transition model.