• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2006년도 추계학술대회논문집
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• pp.65-68
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• 2006

Experiments were performed to investigate the effects of mud-flap width on the aeroacoustic noise generation inside high-speed trains. The open-circuit type wind tunnel was used. The measurement setup was custom-built to simulate intercoach spacing. From the measurements, the characteristics of the turbulent flow after the intercoach spacing and consequent generation of aeroacoustic noise inside the cabin was investigated. Especially the effects of mud flap length on the characteristics of the characteristics of the turbulent flow were identified. The mechanism of noise generation by analyzed interactions with structure vibration characteristics and generation characteristics of blocked pressure was investigated.

• 대한전기학회논문지:전력기술부문A
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• 제53권11호
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• pp.589-595
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• 2004

This paper presents a modeling and simulation of a grid-connected wind turbine generation system with respect to wind variations, starting of large induction motor and three-phase fault in the system, and investigates voltage variations of the system for disturbances. It describes the modeling of the wind turbine system including the drive train model, induction generator model, and grid-interface model on MATLAB/Simulink. The simulation results show the variation of the generator torque, the generator rotor speed, the pitch angle, terminal voltage, system voltage, fault current, and real/reactive power output, etc. Case studies demonstrate that the pitch angle control is carried out to achieve maximum power extraction for wind speed variations, starting of a large induction motor causes a voltage sag due to a large starting current, and a fault on the system influences on the output of the wind turbine generator.

• 신재생에너지
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• 제18권2호
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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.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2007년도 제38회 하계학술대회
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• pp.772-773
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• 2007

This study suggests a modeling of grid-connected wind power generation system that has induction generator, and aims to perform simulations for outputs by the variation of actual wind speed and for fault current of wind generation system by the transformer winding connection. This study is implemented by matlab&simulink. The simulation shall be performed by assuming single line to ground fault generated in the system. Generator power, rotor speed, terminal voltage, system voltage, and fault current shall be observed following the performance of simulation. The fault current change will be dealt through the simulation results for fault current of wind generation system following the grid-connected transformer winding connection and the simulation result by the transformer neutral ground method.

• 경영과학
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• 제30권2호
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• pp.97-106
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• 2013

Of the new and renewable energies currently being pursued domestically, wind energy, together with solar photovoltaic energy, is a new core growth driver industry of Korea. As of May 2012, 33 wind farms at a capacity of 347.8MW are in operation domestically. The purpose of this study was to compare and analyze how efficiently each operational wind farm is utilizing its power generation capacity and the weather resource of wind. For this purpose, the study proceeded in 3 phases. In phase 1, ANOVA analysis was performed for each wind farm, thereby categorizing farms according to capacity, region, generator manufacturer, and quantity of weather resources available and comparing and analyzing the differences among their operating efficiency. In phase 2, for comparative analysis of the operating efficiency of each farm, Data Envelopment Analysis (DEA) was used to calculate the efficiency index of individual farms. In the final phase, phase 3, regression analysis was used to analyze the effects of weather resources and the operating efficiency of the wind farm on the power generation per unit equipment. Results shows that for wind power generation, only a few farms had relatively high levels of operating efficiency, with most having low efficiency. Regression analysis showed that for wind farms, a 1 hour increase in wind speeds of at least 3m/s resulted in an average increase of 0.0000045MWh in power generation per 1MW generator equipment capacity, and a unit increase in the efficiency scale was found to result in approximately 0.20MWh power generation improvement per unit equipment.

• 신재생에너지
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• 제2권2호
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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.

• Journal of Electrical Engineering and Technology
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• 제9권1호
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• pp.205-213
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• 2014

A wind generator (WG) maximum power point tracking (MPPT) system is presented here. It comprises of a variable-speed wind generator, a high-efficiency boost-type dc/dc converter and a control unit. The advantages of the aimed system are that it does not call for the knowledge of the wind speed or the optimal power characteristics and that it operates at a variable speed, thus providing high efficiency. The WG operates at variable speed and thus suffers lower stress on the shafts and gears compared to constant-speed systems. It results in a better exploitation of the available wind energy, especially in the low wind-speed range of 2.5-4.5 m/s. It does not depend on the WG wind and rotor-speed ratings or the dc/dc converter power rating. Higher reliability, lower complexity and cost, and less mechanical stress of the WG. It can be applied to battery-charging applications.

• 전기학회논문지
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• 제61권2호
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• pp.231-236
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• 2012

This paper presents the MPPT(Maximum Power Point Tracking)control method of the PMSG wind generation system using the turbine model with a squirrel cage induction motor. The torque of squirrel cage induction turbine model is controlled by mathematization of speed characteristics of real blade. In this paper, maintenance and cost issues into consideration, except for previous method using information of the velocity of the wind speed sensor, the algorithm is presented. The algorithm is controlled by tracking the optimal point, the generator speed and maximum grid power. The vector controls of the generator side converter and the grid side converter are controlled respectively to obtain maximum torque and regulate unity power factor. With Psim simulations and experiments, the efficiency of squirrel cage induction turbine model and the validity of control algorithm are verified.

• 한국태양에너지학회 논문집
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• 제26권2호
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• pp.27-34
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• 2006

In this paper, a forecasting model of wind speed at Walryong Site, Jeju Island is presented, which has been developed and evaluated as a first step toward establishing Korea Forecasting Model of Wind Power Generation. The forecasting model is constructed based on neural network and is trained with wind speed data observed at Cosan Weather Station located near by Walryong Site. Due to short period of measurements at Walryong Site for training statistical model Gosan Weather Station's long-term data are substituted and then transplanted to Walryong Site by using Measure-Correlate-Predict technique. One to three-hour advance forecasting of wind speed show good agreements with the monitoring data of Walryong site with the correlation factors 0.96 and 0.88, respectively.

• Journal of Electrical Engineering and Technology
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• 제8권3호
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• pp.453-463
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• 2013

The probabilistic nature of renewable energy, especially wind energy, increases the needs for new forms of planning and operating with electrical power. This paper presents a novel approach for determining the short-term generation schedule for optimal operations of wind energy-integrated power systems. The proposed probabilistic security-constrained optimal power flow (P-SCOPF) considers dispatch, network, and security constraints in pre- and post-contingency states. The method considers two sources of uncertainty: power demand and wind speed. The power demand is assumed to follow a normal distribution, while the correlated wind speed is modeled by the Weibull distribution. A Monte Carlo simulation is used to choose input variables of power demand and wind speed from their probability distribution functions. Then, P-SCOPF can be applied to the input variables. This approach was tested on a modified IEEE 30-bus system with two wind farms. The results show that the proposed approach provides information on power system economics, security, and environmental parameters to enable better decision-making by system operators.