• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.18 no.6
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• pp.158-169
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• 2004

A method for predicting the short-term dynamic line ratings in overhead transmission lines using real-time weather forecast data is proposed in this paper. Through some inspections for the 3-hour interval forecasting factors such as ambient temperature, wind speed grade and weather code given by KMA(Korea Meteorological Administration), correlation properties between forecast weather data and actual measured data are analyzed. To use these variable in determining the dynamic line ratings, they are changed into suitable numerical values. Furthermore adaptive neuro-fuzzy systems to improve reliabilities for wind speed and solar heat radiation ate designed It was verified that the forecast weather data can be used to predict the line rating with reliable. As a result it can be possible that the proposed predicting system can be effectively utilized by their anticipation a short-time in advance.

• Journal of Environmental Science International
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• v.14 no.11
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• pp.1015-1026
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• 2005

The southeastern coastal area of the Korean peninsula has a complex terrain including an irregular coastline and moderately high mountains. This implies that mesoscale circulations such as mountain-valley breeze and land-sea breeze can play an important role in wind field and ocean forcing. In this study, to improve the accuracy of complex coastal rind field(surface wind and sea surface wind), we carried out the sensitivity experiments based on PBL schemes in PSU/NCAR Mesoscale Model (MM5), which is being used in the operational system at Korea Meteorological Administration. Four widely used PBL parameterization schemes in sensitivity experiments were chosen: Medium-Range Forecast (MRF), High-resolution Blackadar, Eta, and Gayno-Seaman scheme. Thereafter, case(2004. 8. 26 - 8. 27) of weak-gradient flows was simulated, and the time series and the vertical profiles of the simulated wind speed and wind direction were compared with those of hourly surface observations (AWS, BUOY) and QuikSCAT data. In the simulated results, the strength of rind speed of all schemes was overestimated in complex coastal regions, while that of about four different schemes was underestimated in islands and over the sea. Sea surface wind using the Eta scheme showed the highest wind speed over the sea and its distribution was similar to the observational data. Horizontal distribution of the simulated wind direction was very similar to that of real observational data in case of all schemes. Simulated and observed vertical distribution of wind field was also similar under boundary layer(about 1 km), however the simulated wind speed was underestimated in upper layer.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.24 no.12
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• pp.138-146
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• 2010

On wind turbine generators, the speed and volume of the wind affect the turbine angle speed which finally determines the output level of the electric power. However it is very difficult to forecast correctly the future power output and quality based on previous fixed sampling methods. This paper proposes a variable sampling method based on Buneman frequency estimation algorithm to reflect the variations of the frequency and amplitude on wind power outputs. The proposed method is also verified through the performance test by comparing with the results from previous fixed sampling methods and the real measurement data.

• Journal of Korean Society for Atmospheric Environment
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• v.19 no.5
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• pp.595-610
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• 2003

The statistical indexes such as RMSE (Root Mean Square Error), Mean Bias error, and IOA (Index of agreement) are used to evaluate 3 Dimensional wind and temperature fields predicted by operational meteorological model RAMS (Regional Atmospheric Meteorological System) implemented in CARIS (Chemical Accident Response Information System) for the dispersion forecast of hazardous chemicals in case of the chemical accidents in Korea. The operational atmospheric model, RAMS in CARIS are designed to use GDAPS, GTS, and AWS meteorological data obtained from KMA (Korean Meteorological Administration) for the generation of 3-dimensional initial meteorological fields. The predicted meteorological variables such as wind speed, wind direction, temperature, and precipitation amount, during 19 ∼ 23, August 2002, are extracted at the nearest grid point to the meteorological monitoring sites, and validated against the observations located over the Korean peninsula. The results show that Mean bias and Root Mean Square Error are 0.9 (m/s), 1.85 (m/s) for wind speed at 10 m above the ground, respectively, and 1.45 ($^{\circ}C$), 2.82 ($^{\circ}C$) for surface temperature. Of particular interest is the distribution of forecasting error predicted by RAMS with respect to the altitude; relatively smaller error is found in the near-surface atmosphere for wind and temperature fields, while it grows larger as the altitude increases. Overall, some of the overpredictions in comparisons with the observations are detected for wind and temperature fields, whereas relatively small errors are found in the near-surface atmosphere. This discrepancies are partly attributed to the oversimplified spacing of soil, soil contents and initial temperature fields, suggesting some improvement could probably be gained if the sub-grid scale nature of moisture and temperature fields was taken into account. However, IOA values for the wind field (0.62) as well as temperature field (0.78) is greater than the 'good' value criteria (> 0.5) implied by other studies. The good value of IOA along with relatively small wind field error in the near surface atmosphere implies that, on the basis of current meteorological data for initial fields, RAMS has good potentials to be used as a operational meteorological model in predicting the urban or local scale 3-dimensional wind fields for the dispersion forecast in association with hazardous chemical releases in Korea.

• Wind and Structures
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• v.6 no.3
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• pp.221-248
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• 2003

The design of high-rise building is often influenced by wind-induced motions such as accelerations and lateral deflections. Consequently, the building's structural stiffness and dynamic (vibration periods and damping) properties become important parameters in the determination of such motions. The approximate methods and empirical expressions used to quantify these parameters at the design phase tend to yield values significantly different from each other. In view of this, there is a need to examine how actual buildings in the field respond to dynamic wind loading in order to ascertain a more realistic model for the dynamic behavior of buildings. This paper describes the findings from full-scale measurements of the wind-induced response of typical high-rise buildings in Singapore, and recommends an empirical forecast model for periods of vibration of typical buildings in Singapore, an appropriate computer model for determining the periods of vibration, and appropriate expressions which relate the wind speed to accelerations in buildings based on wind tunnel force balance model test and field results.

• Journal of Electrical Engineering and Technology
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• v.12 no.4
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• pp.1397-1405
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• 2017

As the introduction of wind power is steadily increasing, negative effects of wind power become more important. To operate a power system more reliable, the system operator needs to recognize the maximum required capacity of available generators for a certain period. For recognizing the maximum capacity, this paper proposes a methodology to determine an optimal reserve requirement considering wind power, for the certain period in the mid-term perspective. As wind speed is predicted earlier, the difference of the forecasted and the actual wind speed becomes greater. All possible forecast errors should be considered in determining optimal reserve, and they are represented explicitly by the proposed matrix form in this paper. In addition, impacts of the generator failure are also analyzed using the matrix form. Through three main stages which are the scheduling, contingency and evaluation stages, costs associated with power generation, reserve procurement and the usage, and the reliability cost are calculated. The optimal reserve requirement is determined so as to minimize the sum of these costs based on the cost/reliability analysis. In case study, it is performed to analyze the impact of wind power penetration on the reserve requirement, and how major factors affect it.

• Journal of Korean Society for Atmospheric Environment
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• v.22 no.3
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• pp.309-323
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• 2006

We focus on the improvement in the simulation of sea surface wind over complex coastal area located in the southeastern Korea. In this study, it was carried out sensitivity experiment based on PBL schemes and dynamic frame of MM5 and WRF. Two widely used PBL parameterization schemes were chosen : Medium-Range Forecast (MRF) and Mellor-Yamada-Janjic (MYJ). Thereafter, two cases of sea fog days with weak wind speed and typhoon days with strong wind speed were simulated and analyzed. The result of experiments indicated that wind fold of WRF model was shown more similar distribution with observational data, compared with that of MM5. Simulation of sea surface wind during sea fog days with weak wind speed and typhoon days with strong wind speed were shown similar horizontal distribution with observational data using MYJ and MRF PBL schemes of WRF model, respectively. Horizontal distribution of sea surface wind was more sensitive according to dynamic frame and PBL Schemes of model during sea fog days and typhoon days, respectively.

• Proceedings of the Korea Air Pollution Research Association Conference
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• 2006.04a
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• pp.569-570
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• 2006

• Journal of Environmental Science International
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• v.19 no.8
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• pp.983-999
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• 2010

Wind power energy is one of the favorable and fast growing renewable energies. It is most important for exact analysis of wind to evaluate and forecast the wind power energy. The purpose of this study is to improve the performance of numerical atmospheric model by data assimilation over a complex coastal area. The benefit of the profiler is its high temporal resolution and dense observation data at the lower troposphere. Three wind profiler sites used in this study are inhomogeneously situated near south-western coastal area of Korean Peninsula. The method of the data assimilation for using the profiler to the model simulation is the three-dimensional variational data assimilation (3DVAR). The experiment of two cases, with/without assimilation, were conducted for how to effect on model results with wind profiler data. It was found that the assimilated case shows the more reasonable results than the other case compared with vertical observation and surface Automatic Weather Station(AWS) data. Although the effect of sonde data was better than profiler at a higher altitude, the profiler data improves the model performance at lower atmosphere. Comparison with the results of 4 June and 5 June suggests that the efficiency with hourly assimilated profiler data is strongly influenced by synoptic conditions. The reduction rate of Normalized Mean Error(NME), mean bias normalized by averaged wind speed of observation, on 4 June was 28% which was larger than 13% of 5 June. In order to examine the difference in wind power energy, the wind power density(WPD) was calculated and compared.

• 한국신재생에너지학회:학술대회논문집
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• 2006.06a
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• pp.323-324
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• 2006

In the present paper a forecasting system of wind power generation for Walryong Site, Jejudo is presented, which has been developed and evaluated as a first step toward establishing Korea Forecasting Model of Wind Power Generation. The forecasting model, KIER forecaster is constructed based on statistical models and is trained with wind speed data observed at Gosan Weather Station nearby Walryong Si to. Due to short period of measurements at Walryong Site for training statistical model, Gosan wind data were substituted and transplanted to Walryong Site by using Measure-Correlate-Predict technique. Three-hour advanced forecast ins shows good agreement with the measurement at Walryong site with the correlation factor 0.88 and MAE(mean absolute error) 15% under.