• Proceedings of the KIEE Conference
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• 2015.07a
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• pp.247-248
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• 2015

Wind turbines causes instabilities on the grid as their penetration increase. To mitigate harmful effects from wind turbines, transmission system operator(TSO) set up some requirements to obligate for wind generation operator for grid connection. So wind farm management system(WFMS) has important role to follow requirement from TSO, WFMS calculates available real power by considering wake effects, and dispatches real power order to each wind turbine in wind farm to optimize for decreasing load fatigue. To verify operation of WFMS, real-time simulator(RTS) is necessary. This paper deals with RTS configuration to verify WFMS operation. RTS includes wind farm model and power flow code. Normally, wind farm equivalent simple model makes wind turbines in wind farm to one wind turbine mode which cannot verify power flow in wind farm and WFMS operation. Thus, this paper makes wind farm model using simple wind turbine model with transfer function. Matlab is used for make power flow code and wind farm model to impose RTS and those model is certified by PSCAD/EMTDC.

• International Journal of Naval Architecture and Ocean Engineering
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• v.11 no.2
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• pp.980-992
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• 2019

This paper describes a model test and numerical simulation of a 750-kW-semi-submersible platform wind turbine under several wind and wave conditions for validation of the numerical simulation model. The semi-submersible platform was designed to support the 750-kW-wind turbine class and operate at a water depth of 50 m. The model tests were performed to estimate the performance characteristics of the wind turbine system in the wide tank of the University of Ulsan. Motions and loads of the wind turbine system under the wind and wave conditions were measured and analyzed. The NREL-FAST code was used to simulate the wind turbine system, and the results were compared with those of the test model. The results demonstrate that the numerical simulation captures noticeably the fully coupled floating wind turbine dynamic responses. Also, the model shows a good stability and small responses during waves, wind, and operation of the 750-kW-floating offshore wind turbine.

• Wind and Structures
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• v.17 no.6
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• pp.589-608
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• 2013

A model is proposed to analyse the along-wind dynamic response of upwind turbines with horizontal axis under service wind conditions. The model takes into account the dynamic coupling effect between rotor blades and supporting tower. The wind speed field is decomposed into a mean component, accounting for the well-known wind shear effect, and a fluctuating component, treated through a spectral approach. Accordingly, the so-called rotationally sampled spectra are introduced for the blades to account for the effect of their rotating motion. Wind forces acting on the rotor blades are calculated according to the blade element momentum model. The tower shadow effect is also included in the present model. Two examples of a large and medium size wind turbines are modelled, and their dynamic response is analysed and compared with the results of a conventional static analysis.

• Wind and Structures
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• v.28 no.4
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• pp.215-224
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• 2019

Because of the particularity and complexity of direct air-cooling structures (ACS), wind parameters given in the general load codes are not suitable for the wind-resistant design. In order to investigate the wind loads of ACS, two 1/150 scaled three-span models were designed and fabricated, corresponding to a rigid model and an aero-elastic model, and wind tunnel tests were then carried out. The model used for testing the wind pressure distribution of the ACS was defined as the rigid model in this paper, and the stiffness of which was higher than that of the aero-elastic model. By testing the rigid model, the wind pressure distribution of the ACS model was studied, the shape coefficients of "A" shaped frame and windbreak walls, and the gust factor of the windbreak walls were determined. Through testing the aero-elastic model, the wind-induced dynamic responses of the ACS model was studied, and the wind vibration coefficients of ACS were determined based on the experimental displacement responses. The factors including wind direction angle and rotation of fan were taken into account in this test. The results indicated that the influence of running fans could be ignored in the structural design of ACS, and the wind direction angle had a certain effect on the parameters. Moreover, the shielding effect of windbreak walls induced that wind loads of the "A" shaped frame were all suction. Subsequently, based on the design formula of wind loads in accordance with the Chinese load code, the corresponding parameters were presented as a reference for wind-resistant design and wind load calculation of air-cooling structures.

• 한국태양에너지학회:학술대회논문집
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• 2008.11a
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• pp.288-293
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• 2008

A predictive model of wind speed in the wind farm has very important meanings. This paper presents an estimation model of wind speed based on time series analysis using the observed wind data at Hangyeong Wind Farm in Jeju island, and verification of the predictive model. In case of Hangyeong Wind Farm and Haengwon Wind Farm, The ARIMA(Autoregressive Integrated Moving Average) predictive model was appropriate, and the wind speed estimation model was developed by means of parametric estimation using Maximum likelihood Estimation.

• Wind and Structures
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• v.15 no.1
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• pp.43-64
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• 2012

Many potential small wind turbine locations are near obstacles such as buildings and shelterbelts, which can have a significant, detrimental effect on the local wind climate. A neural network-based model has been developed which predicts mean wind speed and turbulence intensity at points in an obstacle's region of influence, relative to unsheltered conditions. The neural network was trained using measurements collected in the wakes of 18 scale building models exposed to a simulated rural atmospheric boundary layer in a wind tunnel. The model obstacles covered a range of heights, widths, depths, and roof pitches typical of rural buildings. A field experiment was conducted using three unique full scale obstacles to validate model predictions and wind tunnel measurements. The accuracy of the neural network model varies with the quantity predicted and position in the obstacle wake. In general, predictions of mean velocity deficit in the far wake region are most accurate. The overall estimated mean uncertainties associated with model predictions of normalized mean wind speed and turbulence intensity are 4.9% and 12.8%, respectively.

• Journal of Electrical Engineering and Technology
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• v.9 no.6
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• pp.2134-2141
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• 2014

This paper addresses on a wind power system with BESS(Battery Energy Storage System). The concerned system consists of four parts: the wind speed production model, the wind turbine model, configure capacity of the battery energy storage, battery model and control of the BESS. First of all, we produce wind speed by 4-component composite wind speed model. Secondly, the maximum available wind power is determined by analyzing the produced wind speed and the characteristic curve of wind power. Thirdly, we configure capacity of the BESS according to wind speed and characteristic curve of wind speed-power. Then, we propose a control strategy to track the power reference. Finally, some simulations have been demonstrated to visualize the feasibility of the proposed methodology.

• Wind and Structures
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• v.31 no.4
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• pp.321-334
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• 2020

Engineering type tropical cyclone (TC) wind field models are used to estimate TC wind hazard. Some of the models are well-calibrated using observation data, while others are not extensively compared and verified. They are all proxies to the real TC wind fields. The computational effort for their use differs. In the present study, a comparison of the predicted wind fields is presented by considering three commonly used models: the gradient wind field model, slab-resolving model, and a linear height-resolving model. These models essentially predict the horizontal wind speed at a different height. The gradient wind field model and linear height-resolving model are simple to use while the nonlinear slab-resolving model is more compute-intensive. A set of factors is estimated and recommended such that the estimated TC wind hazard by using these models becomes more consistent. The use of the models, including the developed set of factors, for estimating TC wind hazard over-water and over-land is presented by considering the historical tracks for a few sites. It is shown that the annual maximum TC wind speed can be adequately modelled by the generalized extreme value distribution.

• Journal of Environmental Science International
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• v.19 no.10
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• pp.1229-1235
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• 2010

Three meteor-statistical forecasting models - the transfer function model, the time-series autoregressive model and the neural networks model - were tested to develop a daily forecasting model for Jejudo, where the need and demand for wind power forecasting has increased. All the meteorological observation sites in Jejudo have been classified into 6 groups using a cluster analysis. Four pairs of observation sites among them, all having strong wind speed correlation within the same meteorological group, were chosen for a model test. In the development of the wind speed forecasting model for Jejudo, it was confirmed that not only the use a wind dataset at the objective site itself, but the introduction of another wind dataset at the nearest site having a strong wind speed correlation within the same group, would enhance the goodness to fit of the forecasting. A transfer function model and a neural network model were also confirmed to offer reliable predictions, with the similar goodness to fit level.

• Smart Structures and Systems
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• v.21 no.5
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• pp.591-600
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• 2018

The probabilistic characterization of wind field characteristics is a significant task for fatigue reliability assessment of long-span railway bridges in wind-prone regions. In consideration of the effect of wind direction, the stochastic properties of wind field should be represented by a bivariate statistical model of wind speed and direction. This paper presents the construction of the bivariate model of wind speed and direction at the site of a railway arch bridge by use of the long-term structural health monitoring (SHM) data. The wind characteristics are derived by analyzing the real-time wind monitoring data, such as the mean wind speed and direction, turbulence intensity, turbulence integral scale, and power spectral density. A sequential quadratic programming (SQP) algorithm-based finite mixture modeling method is proposed to formulate the joint distribution model of wind speed and direction. For the probability density function (PDF) of wind speed, a double-parameter Weibull distribution function is utilized, and a von Mises distribution function is applied to represent the PDF of wind direction. The SQP algorithm with multi-start points is used to estimate the parameters in the bivariate model, namely Weibull-von Mises mixture model. One-year wind monitoring data are selected to validate the effectiveness of the proposed modeling method. The optimal model is jointly evaluated by the Bayesian information criterion (BIC) and coefficient of determination, $R^2$. The obtained results indicate that the proposed SQP algorithm-based finite mixture modeling method can effectively establish the bivariate model of wind speed and direction. The established bivariate model of wind speed and direction will facilitate the wind-induced fatigue reliability assessment of long-span bridges.