• Proceedings of the KIEE Conference
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• 2008.07a
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• pp.211-212
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• 2008

As a result of environmental concerns, the production of electricity through renewable energy resources is rapidly increasing. Wind energy is among the fastest growing renewable energy resources now being integrated in the power system, and the penetration rate of wind generation has been gradually increased. For power flow analysis of the recent systems, thus, steady-state modeling of wind turbines and their application are of great importance. This paper presents the procedure we applied for implementation of a steady-state wind turbine model in power flow.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.25 no.7
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• pp.97-104
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• 2011

This paper presents modeling and control for the emerging IGBT converter-based high-voltage direct-current system (IGBT-HVDC). This paper adds to the representation of the IGBT-HVDC system in the dq-synchronous reference frame and its decoupled control scheme. Additionally, since the IGBT-HVDC is able to actively support the grid due to its capacity to control independently active and reactive power production, a reactive power control scheme is presented in order to regulate/contribute to the voltage at a remote location by taking into account its operational state and limits. The ability of the control scheme is assessed and discussed by means of simulations using ahybrid power system, which consists of a permanent magnetic synchronous-generator (PMSG) based wind turbine, an IGBT-HVDC, and a local load.

• Journal of Electrical Engineering and Technology
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• v.9 no.1
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• pp.60-70
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• 2014

This paper discusses the impacts of large scale grid-connected wind farm equipped with permanent magnet synchronous generator (PMSG) on power system small signal stability (SSS) incorporating wind generation uncertainty and volatility. Firstly, a practical simplified PMSG model with rotor-flux-oriented control strategy applied is derived. In modeling PMSG generator side converter, the generator-voltage-oriented control strategy is utilized to implement the decoupled control of active and reactive power output. In modeling PMSG grid side converter, the grid-voltage-oriented control strategy is applied to realize the control of DC link voltage and the reactive power regulation. Based on the Weibull distribution of wind speed, the Monte Carlo simulation technique based is carried out on the IEEE 16-generator-68-bus test system as benchmark to study the impacts of wind generation uncertainty and volatility on small signal stability. Finally, some preliminary conclusions and comments are given.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.24 no.5
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• pp.363-373
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• 2014

In this study, computational multi-body dynamic analyses for the drivetrain system of a 5 MW class offshore wind turbine have been conducted using efficient equivalent modeling technique based on the design guideline of GL 2010. The present drivetrain system is originally modeled and its related system data is adopted from the NREL 5 MW wind turbine model. Efficient computational method for the drivetrain system dynamics is proposed based on an international guideline for the certification of wind turbine. Structural dynamic behaviors of drivetrain system with blade, hub, shaft, gearbox, supports, brake disk, coupling, and electric generator have been analyzed and the results for natural frequency and equivalent torsional stiffness of the drivetrain system are presented in detail. It is finally shown that the present multi-body dynamic analysis method gives good agreement with the previous results of the 5 MW class wind turbine system.

• Wind and Structures
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• v.18 no.4
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• pp.391-422
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• 2014

The high frequency force balance (HFFB) technique provides convenient measurements of integrated forces on rigid building models in terms of base bending moments and torque and/or base shear forces. These base moments or forces are then used to approximately estimate the generalized forces of building fundamental modes with mode shape corrections. This paper presents an analysis framework for coupled dynamic response of tall buildings with HFFB technique. The empirical mode shape corrections for generalized forces with coupled mode shapes are validated using measurements of synchronous pressures on a square building surface from a wind tunnel. An alternative approach for estimating the mean and background response components directly using HFFB measurements without mode shape corrections is introduced with a discussion on higher mode contributions. The uncertainty in the mode shape corrections and its influence on predicted responses of buildings with both uncoupled and coupled modal shapes are examined. Furthermore, this paper presents a comparison of aerodynamic base moment spectra with available data sets for various tall building configurations. Finally, e-technology aspects in conjunction with HFFB technique such as web-based on-line analysis framework for buildings with uncoupled mode shapes used in NALD (NatHaz Aerodynamic Loads Database) is discussed, which facilitates the use of HFFB data for preliminary design stages of tall buildings subject to wind loads.

• Journal of IKEEE
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• v.24 no.4
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• pp.1136-1140
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• 2020

As air pollution caused by fine dust becomes serious, interest in the spread of fine dust and prediction of air quality is increasing. The causes of fine dust are very diverse, and some fine dust naturally occurs through forest fires and yellow dust, but most of them are known to be caused by air pollutants from burning fossil fuels such as petroleum and coal or from automobile exhaust gas. In this paper, the CALPUFF model recommended by the US EPA is used, and CALPUFF diffusion modeling is performed by generating a wind field through the CALMET model as a meteorological preprocessing program that generates a three-dimensional wind field, which is a meteorological element required by CALPUFF. Through this, we propose a fine dust diffusion modeling and air quality prediction system that reflects complex topography.

• Asian Journal of Atmospheric Environment
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• v.10 no.2
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• pp.80-89
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• 2016

In the complex terrain where local wind systems are formed, accurate understanding of regional wind variability is required for wind resource assessment. In this paper, cluster analysis based on the similarity of time-series wind vector was applied to classify wind regions with similar wind characteristics and the meteorological validity of regionalization method was evaluated. Wind regions in Jeju Island and Busan were classified using the wind resource map of Korea created by a mesoscale numerical weather prediction modeling. The evaluation was performed by comparing wind speed, wind direction, and wind variability of each wind region. Wind characteristics, such as mean wind speed and prevailing wind direction, in the same wind region were similar and wind characteristics in different wind regions were meteor-statistically distinct. It was able to identify a singular wind region at the top area of Mt. Halla using the inconsistency of wind direction variability. Furthermore, it was found that the regionalization results correspond with the topographic features of Jeju Island and Busan, showing the validity.

• Korean Management Science Review
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• v.33 no.1
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• pp.35-47
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• 2016

Generation of electricity using wind power has received considerable attention worldwide in recent years mainly due to its minimal environmental impact. However, volatility of wind power production causes additional problems to provide reliable electricity to an electrical grid regarding power system operations, power system planning, and wind farm operations. Those problems require appropriate stochastic models for the electricity generation output of wind power. In this study, we review previous literatures for developing the stochastic model for the wind power generation, and propose a systematic procedure for developing a stochastic model. This procedure shows a way to build an ARIMA model of volatile wind power generation using historical data, and we suggest some important considerations. In addition, we apply this procedure into a case study for a wind farm in the Republic of Korea, Shinan wind farm, and shows that our proposed model is helpful for capturing the volatility of wind power generation.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.12 no.2
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• pp.108-112
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• 2012

A sustainable production of electricity is essential for low carbon green growth in South Korea. The generation of wind power as renewable energy has been rapidly growing around the world. Undoubtedly wind energy is unlimited in potential. However, due to its own intermittency and volatility, there are difficulties in the effective harvesting of wind energy and the integration of wind power into the current electric power grid. To cope with this, many works have been done for wind speed and power forecasting. It is reported that, compared with physical persistent models, statistical techniques and computational methods are more useful for short-term forecasting of wind power. Among them, support vector regression (SVR) has much attention in the literature. This paper proposes an SVR based wind speed forecasting. To improve the forecasting accuracy, a fuzzy clustering is adopted in the process of SVR modeling. An illustrative example is also given by using real-world wind farm dataset. According to the experimental results, it is shown that the proposed method provides better forecasts of wind power.

• Wind and Structures
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• v.11 no.1
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• pp.35-50
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• 2008

Wind and temperature have been shown to be the critical sources causing changes in the modal properties of large-scale bridges. While the individual effects of wind and temperature on modal variability have been widely studied, the investigation about the effects of multiple environmental factors on structural modal properties was scarcely reported. This paper addresses the modeling of the simultaneous effects of wind and temperature on the modal frequencies of an instrumented cable-stayed bridge. Making use of the long-term monitoring data from anemometers, temperature sensors and accelerometers, a neural network model is formulated to correlate the modal frequency of each vibration mode with wind speed and temperature simultaneously. Research efforts have been made on enhancing the prediction capability of the neural network model through optimal selection of the number of hidden nodes and an analysis of relative strength of effect (RSE) for input reconstruction. The generalization performance of the formulated model is verified with a set of new testing data that have not been used in formulating the model. It is shown that using the significant components of wind speeds and temperatures rather than the whole measurement components as input to neural network can enhance the prediction capability. For the fundamental mode of the bridge investigated, wind and temperature together apply an overall negative action on the modal frequency, and the change in wind condition contributes less to the modal variability than the change in temperature.