• Wind and Structures
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• v.37 no.2
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• pp.117-131
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• 2023

Korea Design Standard (KDS) will be updated with two major revisions on the assessment of wind load and performance-based wind design (PBWD). Major changes on the wind load assessment are the wind load factor and basic wind speed. Wind load factor in KDS is reduced from 1.3 to 1, and mean recurrence interval (MRI) for basic wind speed increases from 100 years to 500 years considering the reduction of wind load factor. Additional modification is made including pressure coefficient, torsional moment coefficient and spectrum, and aeroelastic instability. Combined effect of the updates of KDS code on the assessment of wind load is discussed with the case study on the specified sites and building. PBWD is newly added in KDS code to consider the cases with various target performance, vortex-induced vibration, aeroelastic instability, or inelastic behavior. Proposed methods and target performance for PBWD in KDS code are introduced.

• Journal of Environmental Science International
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• v.25 no.2
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• pp.247-257
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• 2016

To predict annual energy production (AEP) accurately in the wind farm where located in Seongsan, Jeju Island, Equivalent wind speed (EQ) which can consider vertical wind shear well than Hub height wind speed (HB) is calculated. AEP is produced by CFD model WindSim from National wind resource map. EQ shows a tendency to be underestimated about 2.7% (0.21 m/s) than HB. The difference becomes to be large at nighttime when wind shear is large. EQ can be also affected by atmospheric stability so that is classified by wind shear exponent (${\alpha}$). AEP is increased by 11% when atmosphere becomes to be stabilized (${\alpha}$ > 0.2) than it is convective (${\alpha}$ < 0.1). However, it is found that extreme wind shear (${\alpha}$ > 0.3) is hazardous for power generation. This results represent that AEP calculated by EQ can provide improved accuracy to short-term wind power forecast and wind resource assessment.

• The Journal of the Korea institute of electronic communication sciences
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• v.10 no.8
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• pp.935-944
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• 2015

This study performs the dynamic modeling and the simulation of variable speed wind power system and implements the models of wind speed, wind turbine & PMSG, and MPPT & pitch control as well. The simulation of wind turbine was performed by using the power coefficient and other simulation parameters which were extracted with reference to the commercial 5MW class wind turbine data. As the result of this simulation, MPPT control is confirmed, maintaining the maximum power coefficient as far as the rated speed 12[m/s]. Over 12[m/s] wind speed, this wind power system makes it possible to keep the stable output by controlling the pitch angle.

• The Transactions of the Korean Institute of Power Electronics
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• v.19 no.2
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• pp.149-156
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• 2014

This paper proposes the battery charging algorithm for small-scale wind power generator using three phase PWM converter. it is impossible to control output power of the converter in over wind speed region since back EMF of PMSG is higer than battery voltage. Therefore, battery charging algorithm is proposed to expand battery charging over wind speed region. The suggested method is using the q-axis current for battery charging in the rated wind speed region. In the over wind speed region after it lower back EMF of PMSG using d-axis current it can control output power of the converter. The validity of the proposed algorithm are verified by experiments.

• Smart Structures and Systems
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• v.24 no.6
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• pp.733-744
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• 2019

Wind speed forecasting is critical for a variety of engineering tasks, such as wind energy harvesting, scheduling of a wind power system, and dynamic control of structures (e.g., wind turbine, bridge, and building). Wind speed, which has characteristics of random, nonlinear and uncertainty, is difficult to forecast. Nowadays, machine learning approaches (generalized regression neural network (GRNN), back propagation neural network (BPNN), and extreme learning machine (ELM)) are widely used for wind speed forecasting. In this study, two schemes are proposed to improve the forecasting performance of machine learning approaches. One is that optimization algorithms, i.e., cross validation (CV), genetic algorithm (GA), and particle swarm optimization (PSO), are used to automatically find the optimal model parameters. The other is that the combination of different machine learning methods is proposed by finite mixture (FM) method. Specifically, CV-GRNN, GA-BPNN, PSO-ELM belong to optimization algorithm-assisted machine learning approaches, and FM is a hybrid machine learning approach consisting of GRNN, BPNN, and ELM. The effectiveness of these machine learning methods in wind speed forecasting are fully investigated by one-year field monitoring data, and their performance is comprehensively compared.

• Spatial Information Research
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• v.18 no.3
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• pp.13-22
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• 2010

Due to the frequent gales and typhoons by anomaly climates and its subsequent loss of life and property, the importance of the research estimating wind load is being emphasized when structure is designed. It is necessary to measure geographical information exactly to estimate topographic factor of wind speed because the increase of topographic factor of wind speed means the increase of wind velocity and the increase of wind velocity has an influence on wind load proportionate to a square. Therefore, the accurate and reasonable estimation method of topographic factor of wind speed is presented in this study using ArchiCAD, an architectural BIM(Building Information Modeling) software. When the structure subjected to wind load is designed, reasonability and economic performance of design will be more improved by using the proposed method.

• New & Renewable Energy
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• v.9 no.1
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• pp.12-16
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• 2013

The nacelle anemometer mounted behind the blade roots of a wind turbine measures distorted wind speed comparable with free-stream wind because of the wake effects caused dependent upon the operation of the wind turbine and the rotation of its blades. The field campaign was carried out to measure free-stream wind speed at a height identical to the height of the nacelle anemometer by deploying a ground-based remote-sensing equipment, LIDAR. It is derived that a third-order polynomial equation for correcting wind speed measured by the nacelle anemometer to undistorted free-stream wind speed incident to a wind turbine. It is anticipated that the derived correction equation enables wind speed measured by the nacelle anemometer to be used as a precise input for a wind turbine performance test and for developing an active control logic.

• Wind and Structures
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• v.17 no.4
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• pp.419-433
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• 2013

This paper investigates the vertical profiles of horizontal mean wind speed and direction based on the synchronized measurements from a Doppler radar profiler and an anemometer during 16 tropical cyclones at a coastal site in Hong Kong. The speed profiles with both open sea and hilly exposures were found to follow the log-law below a height of 500 m. Above this height, there was an additional wind speed shear in the profile for hilly upwind terrain. The fitting parameters with both the power-law and the log-law varied with wind strength. The direction profiles were also sensitive to local terrain setups and surrounding topographic features. For a uniform open sea terrain, wind direction veered logarithmically with height from the surface level up to the free atmospheric altitude of about 1200 m. The accumulated veering angle within the whole boundary layer was observed to be $30^{\circ}$. Mean wind direction under other terrain conditions also increased logarithmically with height above 500 m with a trend of rougher exposures corresponding to lager veering angles. A number of empirical parameters for engineering applications were presented, including the speed adjustment factors, power exponents of speed profiles, and veering angle, etc. The objective of this study aims to provide useful information on boundary layer wind characteristics for wind-resistant design of high-rise structures in coastal areas.

• New & Renewable Energy
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• v.18 no.1
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• pp.17-28
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• 2022

This paper presents an effective Markov transition matrix (EMTM), which will be used to calculate the wind speed at the target site in a wind farm to accurately predict wind energy production. The existing MTS prediction method using a Markov transition matrix (MTM) exhibits a limitation where significant prediction variations are observed owing to random selection errors and its bin width. The proposed method selects the effective states of the MTM and refines its bin width to reduce the error of random selection during a gap filling procedure in MTS. The EMTM reduces the level of variation in the repeated prediction of wind speed by using the coefficient of variations and range of variations. In a case study, MTS exhibited better performance than other MCP models when EMTM was applied to estimate a one-day wind speed, by using mean relative and root mean square errors.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.34 no.12
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• pp.1885-1891
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• 2010

Torque control of wind turbines is important when the wind speed is below the rated speed. The main objective of torque control is to extract the maximum power from the potential aerodynamic power of the wind. Torque control methods for wind turbines are classified as torque-mode control and speed-mode control. In torque-mode control, which is well known and traditionally used in many wind turbines, the torque demand of the generator is proportional to the square of the generator speed. In speed-mode control, a PI controller is used to generate the appropriate torque demand of the generator. In this study, the two torque control methods mentioned above are applied to a 2.75-MW wind turbine; simulation results for real turbulence wind speeds are presented, and the response properties are compared.