• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.29 no.7
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• pp.28-37
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• 2015

Owing to the variability of large-scaled wind power system, the development of wind farm management technologies and related compensation methods have been receiving attention. To provide an accurate and reliable output power, certain wind farm adopts a specified management system including a wind prediction model and grid expectation solutions for considering grid condition. Those technologies are focused on improving the reliability and stability issues of wind farms, which can affect not only nearby system devices but also a voltage condition of utility grid. Therefore, to adapt the develop management system, an expectation process about voltage condition of Point of Common Coupling should be integrated in operating system for responding system requirements in real-time basis. This paper introduce a grid imposing method for a real-time based wind farm management system. The expected power can be transferred to the power flow section and the required quantity about reactive power can be calculated through the proposed system. For the verification process, the gauss-seidel method is introduced in the Matlab/Simulink for analysing power flow condition. The entire simulation process was designed to interwork with PSCAD for verifying real power system condition.

• Journal of the Korean Solar Energy Society
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• v.31 no.2
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• pp.72-81
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• 2011

The wind data obtained from an AWS(Automated Weather Station) was used to predict the AEP(annual energy production) of Gangwon wind farm having a total capacity of 98 MWin Korea. A wind energy prediction program based on the Reynolds averaged Navier-Stokes equation was used. Predictions were made for three consecutive years starting from 2007 and the results were compared with the actual AEPs presented in the CDM (Clean Development Mechanism) monitoring report of the wind farm. The results from the prediction program were close to the actual AEPs and the errors were within 7.8%.

• Journal of the Korean Solar Energy Society
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• v.35 no.3
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• pp.17-25
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• 2015

The main objective of this study is to predict the wind power generation at the wind farm using various wake models. Modeling of wind farm is a prerequisite for prediction of annual energy production at the wind farm. In this study, we modeled 20 MW class Seongsan wind farm which has 10 wind turbines located at the eastern part of Jeju Island. WindSim based on the computational fluid dynamics was adopted for the estimation of power generation. The power curve and thrust coefficient with meteorology file were prepared for wind farm modelling. The meteorology file was produced based on the measured data of the Korea Wind Atlas provided by Korea Institute of Energy Research. Three types of wake models such as Jensen, Larsen, and Ishihara et al. wake models were applied to investigate the wake effects. From the result, Jensen and Ishihara wake models show nearly the same value of power generation whereas the Larsen wake model shows the largest value. New positions of wind turbines are proposed to reduce the wake loss, and to increase the annual energy production of the wind farm.

• Journal of the Korean Solar Energy Society
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• v.32 no.6
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• pp.1-10
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• 2012

The annual energy production of Gangwon wind farm was predicted for three consecutive years of 2007, 2008 and 2009 using commercial programs, WindPRO and WindSim which are known to be used the most for wind resource prediction in the world. The predictions from the linear code, WindPRO, were compared with both the actual energy prediction presented in the CDM (Clean Development Mechanism) monitoring report of the wind farm and also the predictions from the CFD code, WindSim. The results from WindPRO were close to the actual energy productions and the errors were within 11.8% unlike the expectation. The reason for the low prediction errors was found to be due to the fact that although the wind farm is located in highly complex terrain, the terrain steepness was smaller than a critical angle($21.8^{\circ}$) in front of the wind farm in the main wind direction. Therefore no flow separation was found to occur within the wind farm. The flow separation of the main wind was found to occur mostly behind the wind farm.

• Journal of Environmental Science International
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• v.23 no.6
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• pp.1085-1093
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• 2014

We investigate the amount of potential electricity energy generated by wind power in Busan metropolitan area, using the mesoscale meteorological model WRF (Weather Research & Forecasting), combined with small wind power generators. The WRF modeling has successfully simulated meteorological characteristics over the urban areas, and showed statistical significant to predict the amount of wind energy generation. The highest amount of wind power energy has been predicted at the coastal area, followed by at riverbank and upland, depending on predicted spatial distributions of wind speed. The electricity energy prediction method in this study is expected to be used for plans of wind farm constructions or the power supplies.

• Journal of Power Electronics
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• v.15 no.4
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• pp.1047-1053
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• 2015

A time-domain simulation tool to predict the dynamic power output of wind turbines in an offshore wind farm was developed in this study. A wind turbine model consisting of first or second order transfer functions of various wind turbine elements was combined with the Ainslie's eddy viscosity wake model to construct the simulation tool. The wind turbine model also includes an aerodynamic model that is a look up table of power and thrust coefficients with respect to the tip speed ratio and pitch angle of the wind turbine obtained by a commercial multi-body dynamics simulation tool. The wake model includes algorithms of superposition of multiple wakes and propagation based on Taylor's frozen turbulence assumption. Torque and pitch control algorithms were implemented in the simulation tool to perform max-Cp and power regulation control of the wind turbines. The simulation tool calculates wind speeds in the two-dimensional domain of the wind farm at the hub height of the wind turbines and yields power outputs from individual wind turbines. The NREL 5MW reference wind turbine was targeted as a wind turbine to obtain parameters for the simulation. To validate the simulation tool, a Danish offshore wind farm with 80 wind turbines was modelled and used to predict the power from the wind farm. A comparison of the prediction with the measured values available in literature showed that the results from the simulation program were fairly close to the measured results in literature except when the wind turbines are congruent with the wind direction.

• Atmosphere
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• v.18 no.3
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• pp.171-182
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• 2008

In this study, we analyzed the spatial and temporal distribution of wind resources over Korea based on hourly observational data recorded over a period of 5 years from 457 stations belonging to Korea Meteorological Administration (KMA). The surface and 850 hPa wind data obtained from the Korea Local Analysis and Prediction System (KLAPS) and the Regional Data Assimilation and Prediction System (RDAPS) over a period of 1 year are used as supplementary data sources. Wind speed is generally high over seashores, mountains, and islands. In 62 (13.5%) stations, mean wind speeds for 5 years are greater than $3ms^{-1}$. The effects of seasonal wind, land-sea breeze, and mountain-valley winds on wind resources over Korea are evaluated as follows: First, wind is weak during summer, particularly over the Sobaek Mountains. However, over the coastal region of the Gyeongnam-province, strong southwesterly winds are observed during summer owing to monsoon currents. Second, the wind speed decreases during night-time, particularly over the west coast, where the direction of the land breeze is opposite to that of the large-scale westerlies. Third, winds are not always strong over seashores and highly elevated areas. The wind speed is weaker over the seashore of the Gyeonggi-province than over the other seashores. High wind speed has been observed only at 5 stations out of the 22 high-altitude stations. Detailed information on the wind resources conditions at the 21 stations (15 inland stations and 6 island stations) with high wind speed in Korea, such as the mean wind speed, frequency of wind speed available (WSA) for electricity generation, shape and scale parameters of Weibull distribution, constancy of wind direction, and wind power density (WPD), have also been provided. Among total stations in Korea, the best possible wind resources for electricity generation are available at Gosan in Jeju Island (mean wind speed: $7.77ms^{-1}$, WSA: 92.6%, WPD: $683.9Wm^{-2}$) and at Mt. Gudeok in Busan (mean wind speed: $5.66ms^{-1}$, WSA: 91.0%, WPD: $215.7Wm^{-2}$).

• New & Renewable Energy
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• v.20 no.2
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• pp.44-54
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• 2024

The objective of this study was to contribute to domestic offshore wind farms by reasonably predicting the expected completion time and installed power generation capacity of offshore wind projects in South Korea. Offshore wind power is drastically regarded as a core tool for clean energy transition and industrial decarbonization in the fight against the climate crisis globally. Especially in South Korea offshore wind power is the main tool in partaking in RE100 and K-RE100, and the Korean government aims to install 14.9 GW of offshore wind farms by 2030. However, this seems to have been significantly delayed due to the complex process of obtaining permits for offshore wind power in Korea. Thus, a reasonable prediction of power generation and a timeline for the final construction are imperative. To establish the delay time for permit licenses, classified location factors were included into site analysis. These factors comprised reviews of transmission and military operability, environmental impact assessment, maritime traffic safety examination, wind resource assessment and an analysis of current offshore wind projects. According to the analysis, the majority of offshore wind projects currently being developed in Korea are predicted to be delayed by 3-5 years as they are among the criteria included in key discussion points for obtaining permits. The cumulative installed power capacity and annual power generation after construction are expected to be 37 GW and 97 TWh respectively.

• Journal of Wind Energy
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• v.15 no.1
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• pp.11-20
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• 2024

As the generation capacity of floating offshore wind turbines increases, the wind load applied to each turbine increases. Due to such a high wind load, the capacity of transport equipment (such as tugboats or cranes) required in the transportation and installation phases must be much larger than that of previous small-capacity wind power generation systems. However, for such an important wind load prediction method, the simple formula proposed by the classification society is generally used, and prediction through wind tunnel tests or Computational Fluid Dynamics (CFD) is rarely used, especially for a concept or initial design stages. In this study, the wind load of a 10 MW class floating offshore wind turbine was predicted by a simplified formula and compared with results of wind tunnel tests. In addition, the wind load coefficients at each stage of fabrication, transportation, and installation are presented so that it can be used during a concept or initial design stages for similar floating offshore wind turbines.

• Journal of the Korean Solar Energy Society
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• v.30 no.3
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• pp.65-70
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• 2010

As for the present wind power industry, most of the computerization for monitoring and control is based on the traditional development methodology, but it is necessary to improve SCADA system since it has a phenomenon of backlog accumulation in the applicable aspect of back-data as well as in the operational aspect in the future. Especially for a system like offshore wind power where a superintendent cannot reside, it is desirable to operate a remote control system. Therefore, it is essential to establish a monitoring system with appropriate control and monitoring inevitably premised on the integrity and independence of data. As a result, a study was carried out on the modeling of offshore wind power data-centered database. In this paper, a logical data modeling method was proposed and designed to establish the database of offshore wind power. In order for designing the logical data modeling of an offshore wind power system, this study carried out an analysis of design elements for the database of offshore wind power and described considerations and problems as well. Through a comparative analysis of the final database of the newly-designed off-shore wind power system against the existing SCADA System, this study proposed a new direction to bring about progress toward a smart wind power system, showing a possibility of a service-oriented smart wind power system, such as future prediction, hindrance-cause examination and fault analyses, through the database integrating various control signals, geographical information and data about surrounding environments.