• 한국신재생에너지학회:학술대회논문집
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• 2009.11a
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• pp.491-491
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• 2009

Wind turbines produce aerodynamic noise which fluctuate periodically at a blade passing frequency. This sound characteristic is called amplitude modulation, or swishing sound. Several previous studies claimed that this amplitude modulation has a possibility to increase noise annoyance. Thus, this study performed a listening test to find the relationship between the amplitude modulation in wind turbine noise on noise annoyance. The stimuli for the listening test was recorded from a 1.5MW wind turbine in Jeju island. The result of the listening test shows that the amplitude modulation in wind turbine noise significantly increase noise annoyance. Moreover, this study analytically examined the effect of amplitude modulation on noise perception. The result indicates that amplitude modulated sound can be easily perceived even though the background noise level is higher than the sound level of the signal.

• Proceedings of the KIEE Conference
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• 2001.10a
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• pp.289-292
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• 2001

A Dual rotor turbines HAWT/VAWT combined wind turbine system that can drastically enhance the power production capability compared to conventional Single Rotor Turbine HAWT system. The combined system that takes advantage of strong point of both horizontal and vertical Axis wind turbine system developed by a venture firm : KOWINTEC of Chonbuk National University. The HAWT/VAWT hybrid system has been successfully field tested and commercial operation since Feb. 12, 2001 in Hae-chang rest park, Bu-an county near the Sae Man-Kum Sea Dike. This paper will briefly describe the field test results performance and a special aerodynamic structure with bevel-planetary gear box of Dual Rotor Wind Turbine system.

• Journal of Power Electronics
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• v.11 no.3
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• pp.342-349
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• 2011

We propose a torque and pitch control scheme for variable speed wind turbines with permanent magnet synchronous generator (PMSG). A torque controller is designed to maximize the power below the rated wind speed and a pitch controller is designed to regulate the output power above the rated wind speed. The controllers exploit the sliding mode control scheme considering the variation of wind speed. Since the aerodynamic torque and rotor acceleration are difficult to measure in practice, a finite time convergent observer is designed which estimates them. In order to verify the proposed control strategy, we present stability analysis as well as simulation results.

• Proceedings of the KIPE Conference
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• 2012.07a
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• pp.134-135
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• 2012

This paper analyzes the characteristics of the power system of Jeju island in 2014, which has wind farms with the support of BESSs (Battery Energy Storage Systems). In the simulation, the electrical loads are predicted based on Korea Power Exchange's data and the wind turbines are considered with new installed plans within 2014. The situation that some wind farms are forced to disconnect from the grid instantaneously is considered. The BESSs are controlled by using SOC (State of Charge) and power smoothing control algorithm. The simulation results show the effectiveness of the proposed method.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.2
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• pp.247-252
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• 2016

The latest offshore wind turbines are easily exposed to lightning strikes because they are designed with longer blades and taller height to satisfy the growing capacity demands. The generation facilities and elements of the offshore wind farm are more vulnerable to lightning damage because of more severe, unpredictable weather conditions. Therefore, this paper presents the analysis of measure for lightning overvoltage mitigation in offshore wind farm planned in South Korea southwest seashore. The sensitivity analysis includes the steady state and transient state characteristics of offshore wind farm and proposes the countermeasure for mitigation of transient overvoltage by considering earth resistivity of the offshore environment.

• Wind and Structures
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• v.28 no.3
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• pp.141-153
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• 2019

In this study, we have focused on commonly used 14 different small wind turbine airfoils (A18, BW3, Clark Y, E387, FX77, NACA 2414, RG 15, S822, S823, S6062, S7012, SD6060, SD7032, SD7062). The main purpose of the study is to determine the lift, drag and lift/drag coefficients of these airfoils with numerical analysis and to verify 2 best airfoil's results with experimental analysis. Airfoils were determined from past studies on small wind turbines. Numerical analyzes of the airfoils were done with Ansys Fluent fluid dynamics program. Experimental analyzes were done at wind tunnel in Erciyes University, Turkey. Lift and drag coefficients of these airfoils were determined for 50,000-100,000-200,000 Reynolds numbers.

• 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.

• Journal of Wind Energy
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• v.14 no.1
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• pp.37-43
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• 2023

In this study, synthetic time series wind data was generated numerically using a second-order Markov chain. One year of wind data in 2020 measured by the AWS on Wido Island was used to investigate the statistics for measured wind data. Both the transition probability matrix and the cumulative transition probability matrix for annual hourly mean wind speed were obtained through statistical analysis. Probability density distribution along the wind speed and autocorrelation according to time were compared with the first- and the second-order Markov chains with various lengths of time series wind data. Probability density distributions for measured wind data and synthetic wind data using the first- and the second-order Markov chains were also compared to each other. For the case of the second-order Markov chain, some improvement of the autocorrelation was verified. It turns out that the autocorrelation converges to zero according to increasing the wind speed when the data size is sufficiently large. The generation of artificial wind data is expected to be useful as input data for virtual digital twin wind turbines.

• Journal of Wind Energy
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• v.14 no.3
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• pp.14-24
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• 2023

The significance of renewable energy has been on the rise, as evidenced by the 3020 renewable energy plan and the 2050 carbon neutrality strategy, which seek to advance a low-carbon economy by implementing a power supply strategy centered around renewable energy sources. This study examines the wind resources on the west coast of South Korea and confirms the potential for wind power generation in the area. Wind speed data was collected from 22 automatic weather system stations and four light house automatic weather system stations provided by the Korea Meteorological Administration to evaluate potential sites for wind farms. Weibull distribution was used to analyze the wind data and calculate wind power density. Annual energy production and capacity factors were estimated for 15-20 MW-class large wind turbines through the height correction of observed wind speeds. These findings offer valuable information for selecting wind power generation sites, predicting economic feasibility, and determining optimal equipment capacity for future wind power generation sites in the region.

• Journal of Wind Energy
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• v.15 no.2
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• pp.10-22
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• 2024

This research is a comprehensive analysis of wind power prediction sensitivity using a Long Short-Term Memory (LSTM) deep learning neural network model, accounting for the inherent uncertainties in wind speed estimation. Utilizing a year's worth of operational data from an operational wind farm, the study forecasts the power output of both individual wind turbines and the farm collectively. Predictions were made daily at intervals of 10 minutes and 1 hour over a span of three months. The model's forecast accuracy was evaluated by comparing the root mean square error (RMSE), normalized RMSE (NRMSE), and correlation coefficients with actual power output data. Moreover, the research investigated how inaccuracies in wind speed inputs affect the power prediction sensitivity of the model. By simulating wind speed errors within a normal distribution range of 1% to 15%, the study analyzed their influence on the accuracy of power predictions. This investigation provided insights into the required wind speed prediction error rate to achieve an 8% power prediction error threshold, meeting the incentive standards for forecasting systems in renewable energy generation.