• 한국항공운항학회지
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• 제31권3호
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• pp.59-70
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• 2023

In order to predict low-level wind shear at Incheon International Airport (RKSI), a Low-Level Wind Shear prediction system (KMAP-LLWS) along the runway take-off and landing route at RKSI was established using Korea Meteorological Administration Post-Processing (KMAP). For the performance evaluation, the case of low-level wind shear cases calculated from Aircraft Meteorological Data Relay (AMDAR) from July 2021 to June 2022 was used. As a result of verification using the performance evaluation index, POD, FAR, CSI, and TSS were 0.5, 0.85, 0.13, and 0.34, respectively, and the prediction performance was improved by POD, CSI, and TSS compared to the Low-Level Wind Shear prediction system (LDPS-LLWS) calculated using the Korea Meteorological Administration's Local Data Assimilation and Prediction System (LDAPS). This means that the use of high-resolution numerical models improves the predictability of wind changes. In addition, to improve the high FAR of KMAP-LLWS, the threshold for low-level wind shear strength was adjusted. As a result, the most effective low-level wind shear threshold at 8.5 knot/100 ft was derived. This study suggests that it is possible to predict and respond to low-level wind shear at RKSI. In addition, it will be possible to predict low-level wind shear at other airports without wind shear observation equipment by applying the KMAP-LLWS.

• 대기
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• 제17권4호
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• pp.327-337
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• 2007

In this study, we examined the new ensemble training approach to reduce the systematic error and improve prediction skill of wind by using the Short-range Ensemble prediction system (SENSE), which is the mesoscale multi-model ensemble prediction system. The SENSE has 16 ensemble members based on the MM5, WRF ARW, and WRF NMM. We evaluated the skill of surface wind prediction compared with AWS (Automatic Weather Station) observation during the summer season (June - August, 2006). At first stage, the correction of initial state for each member was performed with respect to the observed values, and the corrected members get the training stage to find out an adaptive weight function, which is formulated by Root Mean Square Vector Error (RMSVE). It was found that the optimal training period was 1-day through the experiments of sensitivity to the training interval. We obtained the weighted ensemble average which reveals smaller errors of the spatial and temporal pattern of wind speed than those of the simple ensemble average.

• 조명전기설비학회논문지
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• 제29권1호
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• pp.86-93
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• 2015

Globally, wind power development is experiencing dramatic growth and wind power penetration levels are increasing. Wind generation is highly variable in time and space and it doesn't guarantee the system reliability and secure system operation. As wind power capacity becomes a significant portion of total generation capacity, the reliability assessment for wind power are therefore needed. At present, this operational reliability assessment is focusing on a generation adequacy perspective and does not consider transmission reliability issues. In this paper, we propose the critical transmission operating constraint prediction(CTOCP) system with high wind power penetration to enhance transmission reliability.

• KEPCO Journal on Electric Power and Energy
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• 제2권4호
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• pp.627-630
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• 2016

Recently, the size of wind farms is becoming larger, and the integration of high wind generation resources into power gird is becoming more important. Due to intermittency of wind generating resources, it is an essential to predict power outputs. In this paper, we introduce the basic concept of curvilinear regression, which is one of the method of wind power prediction. The empirical data, wind farm power output in Jeju Island, is considered to verify the proposed prediction model.

• 동력기계공학회지
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• 제3권1호
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• pp.38-44
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• 1999

Subway is one of the most important transportation and its facilities are increased by the drift of population to cities in these days. But heat generation results from lighting, human and traffic increase in subway, half-closed space, gives uncomfortable sense to the subway passengers. Therefore, natural ventilation by piston effect is done to relieve uncomfortable sense. But train wind by piston effect gives uncomfortable sense to the subway passengers, too. So the numerical calculation of inflow and outflow amounts is important to predict thermal environment and reduce train wind. In case of actual survey of train wind in target station, the amount of train wind are about $3100m^3/train$ at the minimum, about $6000m^3/train$ at the maximum, about $4200m^3/train$ on average. When comparison between simulation for train wind prediction and actual survey for accuracy was done train wind prediction program showed similar results.

• 동력기계공학회지
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• 제21권6호
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• pp.31-39
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• 2017

In order to examine if met-masts wind data can exchange each other for wind resource assessment, an investigation was carried out in Kimnyeong and Haengwon regions of Jeju Island. The two regions are both simple terrain and 4.31 km away from each other. The one-year wind speed data measured by 70 m-high anemometers of each met-mast of the two regions were analysed in detail. Measure-Correlate-Predict (MCP) method was applied to the two regions using the 10-year Automatic Weather System (AWS) wind data of Gujwa region for creating 10-year Wind Statistics by running WindPRO software. The two 10-year Wind Statistics were applied to the self-met mast point for self prediction of Annual Energy Production (AEP) and Capacity Factor (CF) and the each other's met mast point for mutual prediction of them. As a result, when self-prediction values were reference, relative errors of mutual prediction values were less than 1% for AEP and CF so that met masts wind data under the same condition of this study could exchange each other for estimating accurate wind resource.

• 농업과학연구
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• 제47권2호
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• pp.381-394
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• 2020

Accelerated soil wind erosion still remains to date to cause severe economic and environmental impacts. Revised and updated models to quantitatively evaluate wind induced soil erosion have been made for specific factors in the wind erosion equation (WEQ) framework. Because of increasing quantities of accumulated data, the WEQ, the revised wind erosion equation (RWEQ), the wind erosion prediction system (WEPS), and other soil wind erosion models have been established. These soil wind erosion models provide essential knowledge about where and when wind erosion occurs although naturally, they are less accurate than the field-scale. The WEQ was a good empirical model for comparing the effects of various management practices on potential erosion before the RWEQ and the WEPS showed more realistic estimates of erosion using easily measured local soil and climatic variables as inputs. The significant relationship between the observed and predicted transport capacity and soil loss makes the RWEQ a suitable tool for a large scale prediction of the wind erosion potential. WEPS developed to replace the empirical WEQ can calculate soil loss on a daily basis, provide capability to handle nonuniform areas, and obtain predictions for specific areas of interest. However, the challenge of precisely estimating wind erosion at a specific regional scale still remains to date.

• 전기학회논문지
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• 제64권1호
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• pp.107-112
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• 2015

This paper introduces an evolutionary nonlinear regression based compensation technique for the short-range prediction of wind speed using AWS(Automatic Weather Station) data. Development of an efficient MOS(Model Output Statistics) is necessary to correct systematic errors of the model, but a linear regression based MOS is hard to manage an irregular nature of weather prediction. In order to solve the problem, a nonlinear and symbolic regression method using GP(Genetic Programming) is suggested for a development of MOS wind forecast guidance. Also FCM(Fuzzy C-Means) clustering is adopted to mitigate bias of wind speed data. The purpose of this study is to evaluate the accuracy of the estimation by a GP based nonlinear MOS for 3 days prediction of wind speed in South Korean regions. This method is then compared to the UM model and has shown superior results. Data for 2007-2009, 2011 is used for training, and 2012 is used for testing.

• 신재생에너지
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• 제16권2호
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• pp.1-13
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• 2020

The reduction of noise from wind turbines has been studied using various methods. Some examples include controlling wind turbine blades, designing low-noise-emitting wind turbine blades, and using trailing-edge serrations. Among these methods, serration is considered an effective noise reduction method. Various studies have aimed to understand the effects of trailing-edge serration parameters. Most studies, however, have focused on fixed-wing concepts, and few have analyzed noise reduction or developed a prediction method for rotor-type blades. Herein, a noise prediction method, composed of two noise prediction methods for a wind turbine with trailing-edge serrations, is proposed. From the flow information obtained by an in-house program (WINFAS), the noise from non-serrated blades is calculated by turbulent ingestion noise and airfoil self-noise prediction methods. The degree of noise reduction caused by the trailing-edge serrations is predicted in the frequency domain by Lyu's method. The amount of noise reduction is subtracted from the predicted result of the non-serrated blade and the total reduction of the noise from the rotor blades is calculated.

• International Journal of Contents
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• 제10권4호
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• pp.75-82
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• 2014

The purpose of this paper was to identify suitable variables for financial distress prediction models and to compare the accuracy of MDA and LA for early warning signals for wind energy companies in Korea. The research methods, discriminant analysis and logit analysis have been widely used. The data set consisted of 15 wind energy SMEs in KOSDAQ with financial statements in 2012 from KIS-Value. We found that five financial ratio variables were statistically significant and the accuracy of MDA was 86%, while that of LA is 100%. The importance of this study is that it demonstrates empirically that financial distress prediction models are applicable to the wind energy industry in Korea as an early warning signs of impending bankruptcy.