• Journal of the Korean Society for Precision Engineering
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• v.20 no.4
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• pp.84-91
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• 2003

The paper presents a power control algorithm for a full pitch-controlled wind power system. The design of a pitch controller, in general, is performed by linearizing the torque in the vicinity of a operating point assuming the tip speed ratio is constant. For power control, however, the tip speed ratio is no longer a constant. In this study, a reference pitch model is derived in terms of a wind speed, angular velocity, and pitch angle. The reference pitch model is used to design a controller without linearizing the non-linear torque model of the blade. The validity of the algorithm is demonstrated with the results produced through sets of simulation.

• Proceedings of the KIPE Conference
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• 2012.07a
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• pp.128-129
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• 2012

This paper proposed the wind turbine simulator for comparative study of the MPPT controls. The development of this wind turbine simulator is based on the torque controlled induction motor. The torque reference is obtained from a mathematical model of wind turbine whereas the inputs are rotor speed, wind speed and fixed-value of pitch angle. By using this wind turbine simulator, the real wind is not needed. Wind speed information can be stored and regenerated anytime. Hence it is possible to apply the same wind speed condition to different MPPT controls. With the same wind speed condition, it can fairly compare the advantages and disadvantages of the MPPT controls. The proposed wind turbine simulator is verified through PSIM simulation.

• Proceedings of the KIPE Conference
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• 2006.06a
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• pp.345-347
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• 2006

In this paper, a novel method for wind speed estimation in wind power generation systems is presented. The proposed algorithm is based on estimating the wind speed using Support-Vector-Machines for regression (SVR). The wind speed is estimated using the generator power-speed characteristics as a set of training vectors. SVR is trained off-line to predict a continuos-valued function between the system's inputs and wind speed value. The predicted off-line function as well as the instantaneous generator power and speed are then used to determine the unknown winds speed on-line. The simulation results show that SVR can define the corresponding wind speed rapidly and accurately to determine the optimum generator speed reference for maximum power point tracking.

• Journal of Electrical Engineering and Technology
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• v.11 no.6
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• pp.1548-1555
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• 2016

This paper presents an application of time series analysis in hourly wind speed simulation and forecast in Jeju Island, Korea. Autoregressive - moving average (ARMA) model, which is well in description of random data characteristics, is used to analyze historical wind speed data (from year of 2010 to 2012). The ARMA model requires stationary variables of data is satisfied by power law transformation and standardization. In this study, the autocorrelation analysis, Bayesian information criterion and general least squares algorithm is implemented to identify and estimate parameters of wind speed model. The ARMA (2,1) models, fitted to the wind speed data, simulate reference year and forecast hourly wind speed in Jeju Island.

• 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.

• Journal of Institute of Control, Robotics and Systems
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• v.15 no.8
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• pp.859-866
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• 2009

The article discusses an issue of estimating the airspeed of an autonomous flying vehicle. Airspeed is the difference between ground speed and wind speed. It is desirable to know any two among the three speeds for navigation, guidance and control of an autonomous vehicle. For example, ground speed and position are used to guide a vehicle to a target point and wind speed and airspeed are used to maximize flight performance such as a gliding range. However, the target vehicle has not an airspeed sensor but a ground speed sensor (GPS/INS). So airspeed or wind speed has to be estimated. Here, airspeed is to be estimated. A vehicle's dynamics and its dynamic parameters are used to estimate airspeed with attitude and angular speed measurements. Kalman filter is used for the estimation. There are also two major sources arousing a robust estimation problem; wind speed and altitude. Wind speed and direction depend on weather conditions. Altitude changes as a vehicle glides down to the ground. For one reference altitude, multiple model Kalman filters are pre-designed based on several reference airspeeds. We call this group of filters as a cluster. Filters of a cluster are activated simultaneously and probabilities are calculated for each filter. The probability indicates how much a filter matches with measurements. The final airspeed estimate is calculated by summing all estimates multiplied by probabilities. As a vehicle glides down to the ground, other clusters that have been designed based on other reference altitudes are activated. Some numerical simulations verify that the proposed method is effective to estimate airspeed.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.36-36
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• 2000

The paper presents a speed control algorithm for a full pitch-controlled wind turbine system. Torque of a blade generated by wind energy is non-linear function of a wind speed, angular velocity, and pitch angle of the blade. The design of a cor_troller, in general, is performed by linearizing the torque in the vicinity of a operating point assuming the angular velocity of the blade is constant. For speed control, however, the angular velocity is no longer a constant, so that linearization of the torque in terms of a wind speed and pitch angle is impossible. In this study, a reference pitch model is derived in terms of a wind speed, angular velocity, and pitch angle, which makes it possible to design a controller without linearizing the non-linear torque model of the blade. The validity of the algorithm is demonstrated with the results produced through sets of experiments.

• Proceedings of the KIEE Conference
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• 2005.07b
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• pp.1750-1752
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• 2005

This paper presents a variable speed wind generation system where fuzzy logic controllers is used as efficiency optimizer. The fuzzy logic controller increments the machine flux by on-line search to improve the generator efficiency in case of light load. The speed of the induction generator is controlled according to the variation of the wind speed in order to produce the maximum output power The generator reference speed is adjusted according to the optimum tip-speed ratio. The complete control system has been developed by simulation study.

• Journal of Wind Energy
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• v.9 no.4
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• pp.24-31
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• 2018

Wind lidar application is increasing and its calibration method is required to use wind lidar as an alternative to the meteorological mast. A nacelle lidar calibration method is now being discussed in IEC 61400-50-3 (Wind energy generation systems - Part 50-3: Use of nacelle-mounted lidars for wind measurements), and the method is mainly based on the wind lidar beam line of sight (LOS) wind speed calibration suggested by DTU as DTU E-0020 (Calibrating Nacelle Lidars). In this paper, a LOS wind speed calibration method is introduced and a calibration example performed on Jeju island is presented. The results showed a slope of 1.011 and R2 of 0.997, which means that the LOS wind speed is highly correlated with the reference wind speed and is comparable. But LOS wind speed calibration requires a very long time due to its principle and environmental conditions, and a calibration method that can overcome this problem of uncontrollable environments needs to be developed.

• Journal of the Korean Solar Energy Society
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• v.30 no.4
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• pp.79-85
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• 2010

The uncertainty in WindCube LIDAR measurements, which are specific to wind profiling at less than 200m above ground levelin wind resource assessments, was analyzed focusing on the error caused by its volume sampling principle. A two-month SODAR measurement campaign conducted in an urban environment was adopted as the reference wind profile assuming that various atmospheric boundary layer shapes had been captured. The measurement error of LIDAR at a height z was defined as the difference in the wind speeds between the SODAR reference data, which was assumed to be a virtually true value, and the numerically averaged wind speed for a sampling volume height interval of $z{\pm}12.5m$. The pattern of uncertainty in the measurement was found to have a maximum in the lower part of the atmospheric boundary layer and decreased with increasing height. It was also found that the relative standard deviations of the wind speed error ratios were 6.98, 2.70 and 1.12% at the heights of 50, 100 and 150m above ground level, respectively.