• JOURNAL OF ELECTRICAL WORLD
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• s.409
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• pp.58-62
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• 2011

신재생에너지 사업역량 강화! 현대중공업(회장 민계식)이 그동안 조선업종과 전기전자시스템 분야에서 축적한 경험과 기술을 토대로, 지속적인 성장이 예측되고 있는 풍력분야에 역량을 집중하고 있다. 글로벌 시장 진입을 향한 이 같은 도전은 지난해 3월 풍력업체 최초로 군산풍력발전기공장을 군산산업단지 내에 완공하면서 새로운 전기가 마련되었고, 해외진출을 본격화하기 위한 대량생산체제 구축, 기종 다변화 및 신규모델 개발 등 힘찬 비상의 담금질에 연일 가속도가 붙고 있다. 현대중공업은 오는 2012년까지 군산 공장을 800MW 규모까지 확장시켜 풍력시장 수요에 신속히 대응하는 한편, 세계 풍력발전기 시장의 중심축 역할을 견고히 소화해 내겠다는 포석을 이미 마쳤다. 미래에너지로 주목받고 있는 풍력 및 태양광발전에 꾸준히 투자해온 현대중공업이 그린비지니스 사업 분야 특히, 풍력발전에 집중하는 행보에 관심이 집중되고 있다.

• Proceedings of the Korea Information Processing Society Conference
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• 2012.04a
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• pp.56-59
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• 2012

최근 신재생 에너지 분야 가운데 풍력발전에 대한 관심이 높아지면서 관련 산업 분야의 연구 또한 활발히 수행되고 있다. 풍력발전기의 경우 초기 설치에 고가의 비용이 필요하며, 고장 발생 시 일반적으로 풍력발전기의 동작을 멈추게 한다. 본 논문에서는 풍력발전기의 Nacelle에서 발생하는 다양한 센서데이터를 수집하는 Bottom에서의 제어시스템을 다중화하여 고장 발생 시에도 풍력발전기의 중단 없이 지속적으로 에너지 생산이 가능하게 하는 고가용성 데이터베이스 시스템에 관한 것이다. 본 논문을 통해 개발된 고가용성 시스템을 통해 풍력 발전기의 높은 안정성을 보장하며, 수집된 데이터를 분석함으로서 고장에 대한 예측이 가능하다.

• Journal of the Korea Society for Simulation
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• v.32 no.3
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• pp.33-41
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• 2023

As carbon-free has been getting interest, renewable energy sources have been increasing. However, renewable energy is intermittent and variable so it is difficult to predict the produced electrical energy from a renewable energy source. In this study, digital-twin concept is applied to solve difficulties in predicting electrical energy from a renewable energy source. Considering that rotation of wind turbine has high correlation with produced electrical energy, a model which simulates rotation in the drivetrain of a wind turbine is developed. The base of a drivetrain simulation model is set with well-known state equation in mechanical engineering, which simulates the rotating system. Simulation based machine learning is conducted to get unknown parameters which are not provided by manufacturer. The simulation is repeated and parameters in simulation model are corrected after each simulation by optimization algorithm. The trained simulation model is validated with 27 real wind turbine operation data set. The simulation model shows 4.41% error in average compared to real wind turbine operation data set. Finally, it is assessed that the drivetrain simulation model represents the real wind turbine drivetrain system well. It is expected that wind-energy-prediction accuracy would be improved as wind turbine digital twin including the developed drivetrain simulation model is applied.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.37 no.4
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• pp.423-429
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• 2013

The performance of a vertical-type wind power generator system was predicted by CFD analysis. In the analysis, the reaction torque was calculated for a given rotational speed of the blades. The blade torque of a wind power system was obtained for various rotational speeds, and the generation power was calculated using the obtained torque and the rotational speed. The optimum generator specification, therefore, could be decided using the relationship between the generated power and the rotational speeds. The effects of the number of blades and blade shapes on the generation power were also investigated. Finally, the analysis results were compared with the experimental results.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.40 no.1
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• pp.21-29
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• 2016

The wake effects behind wind turbines were investigated by using data from a Met Mast tower and the SCADA (Supervisory Control and Data Acquisition) system for a wind turbine. The results of the wake investigations and predicted values for the velocity deficit based on the eddy viscosity model were compared with the turbulence intensity from the Lange model. As a result, the velocity deficit and turbulence intensity of the wake increased as the free stream wind speed decreased. In addition, the magnitude of the velocity deficit for the center of the wake using the eddy viscosity model was overestimated while the turbulence intensity from the Lange model showed similarities with measured values.

• 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.

• New & Renewable Energy
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• v.10 no.2
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• pp.19-28
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• 2014

Wind power generation of 5 MW wind turbine was predicted by using wind measurement data from HeMOSU-1 which is at south west coast of Korea. Time histories of turbulent wind was generated from 10-min mean wind speed and then they were used as input to Bladed to estimated electric power. Those estimated powers are used in both polynominal regression and neural network training. They were compared with each other for daily production and yearly production. Effect of mean wind speed and turbulence intensity were quantitatively analyzed and discussed. This technique further can be used to assess lifetime power of wind turbine.

• 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 the Korean Society for Aeronautical & Space Sciences
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• v.33 no.7
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• pp.40-50
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• 2005

In this paper, preliminary results for performance prediction of a dual-rotor wind turbine generator system are presented. Blade element and momentum theories are used to model the aerodynamic forces and moments acting on the rotor blades, and multi-body dynamics approach is used to integrate the major components to represent the overall system. Not only the steady-state performance but the transient response characteristics are analyzed. Pitch control strategy to control the rotor speed and the generator output is proposed and its performance is verified through the nonlinear simulation.

• Journal of the Korean earth science society
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• v.38 no.1
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• pp.11-23
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• 2017

The short term predictability of wind resources is an important factor in evaluating the economic feasibility of a wind power plant. As a method of improving the predictability, a Bayesian Kalman filter is applied as the model data postprocessing. At this time, a statistical training period is needed to evaluate the correlation between estimated model and observation data for several Kalman training periods. This study was quantitatively analyzes for the prediction characteristics according to different training periods. The prediction of the temperature and wind speed with 3-day short term Bayesian Kalman training at Taebaek area is more reasonable than that in applying the other training periods. In contrast, it may produce a good prediction result in Ieodo when applying the training period for more than six days. The prediction performance of a Bayesian Kalman filter is clearly improved in the case in which the Weather Research Forecast (WRF) model prediction performance is poor. On the other hand, the performance improvement of the WRF prediction is weak at the accurate point.