• Journal of Korean Society of Forest Science
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• v.99 no.2
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• pp.197-203
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• 2010

The data of average temperature, average relative humidity, precipitation and average wind speed were collected from 674 meteorological stations in China. A specific procedure that processes original data into a new data format needed in forest fire danger rating forecast system of China was introduced systematically, and the feasibility of this method was validated in this paper. In addition, a set of meteorological data processing software was constructed by the secondary development of GIS in order to realize automation of processing data for the system. Results showed that the approach preformed well in handling temperature, average relative humidity and average wind speed, and the processing effect of precipitation was acceptable. Moreover, the automated procedure could be achieved by GIS and the working efficiency was about 3 times as much as that of manual handling. The informationization level of processing meteorological data was greatly enhanced.

• 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.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.55 no.10
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• pp.426-432
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• 2006

This paper describes the concepts of Static Line Rating (SLR) and Dynamic Line Rating (DLR) and the computational methods to demonstrate them. Calculation of the line capacity needs the heat balance equation which is also used for computing the reduced tension in terms of line aging. SLR is calculated with the data from the worst condition of weather throughout the year. Even now, the utilization ratio is obtained from this SLR data in Korea. DLR is the improved method compared to SLR. A process for DLR reveals not only improved line ratings but also more accurate allowed line ratings based on line aging and real time conditions of weather. In order to reflect overhead transmission line aging in DLR, this paper proposes the method that considers the amount of decreased tension since the lines have been installed. Therefore, the continuous allowed temperature for remaining life time is newly acquired. In order to forecast DLR, this paper uses weather forecast models, and applies the concept of Thermal Overload Risk Probability (TORP). Then, the new concept of Dynamic Utilization Ratio (DUR) is defined, replacing Static Utilization Ratio (SUR). For the case study, the two main transmission lines which are responsible for the north bound power flow in the Seoul metropolitan area are chosen for computing line rating and utilization ratio. And then line rating and utilization ratio are analyzed for each transmission line, so that comparison of the present and estimated utilization ratios becomes available. Finally, this paper proves the validity of predictive DUR as the objective index, with simulations of emergency state caused by system outages, overload and so on.

• Korean Journal of Agricultural and Forest Meteorology
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• v.14 no.1
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• pp.1-10
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• 2012

Digital Forecast of the Korea Meteorological Administration (KMA) represents 5 km gridded weather forecast over the Korean Peninsula and the surrounding oceanic regions in Korean territory. Digital Forecast provides 12 weather forecast elements such as three-hour interval temperature, sky condition, wind direction, wind speed, relative humidity, wave height, probability of precipitation, 12 hour accumulated rain and snow, as well as daily minimum and maximum temperatures. These forecast elements are updated every three-hour for the next 48 hours regularly. The objective of this study was to construct Forest Fire Danger Rating Systems on the Korean Peninsula (FFDRS_KORP) based on the daily weather index (DWI) and to improve the accuracy using the digital forecast data. We produced the thematic maps of temperature, humidity, and wind speed over the Korean Peninsula to analyze DWI. To calculate DWI of the Korean Peninsula it was applied forest fire occurrence probability model by logistic regression analysis, i.e. $[1+{\exp}\{-(2.494+(0.004{\times}T_{max})-(0.008{\times}EF))\}]^{-1}$. The result of verification test among the real-time observatory data, digital forecast and RDAPS data showed that predicting values of the digital forecast advanced more than those of RDAPS data. The results of the comparison with the average forest fire danger rating index (sampled at 233 administrative districts) and those with the digital weather showed higher relative accuracy than those with the RDAPS data. The coefficient of determination of forest fire danger rating was shown as $R^2$=0.854. There was a difference of 0.5 between the national mean fire danger rating index (70) with the application of the real-time observatory data and that with the digital forecast (70.5).

• Proceedings of the KIEE Conference
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• 2005.11b
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• pp.35-38
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• 2005

This paper suggests the method that forecast Dynamic Line Rating (DLR). Thermal Overload Risk (TOR) of next time is forecasted based on current weather condition and DLR value by Monte Carlo Simulation (MCS). To model weather element of transmission line for MCS, we will propose the use of weather forecast system and statistical models that time series law is applied. Also, through case study, forecasted TOR probability confirmed can utilize by standard that decide DLR of next time. In short, proposed method may be used usefully to keep safety of transmission line and reliability of supply of electric Power by forecasting transmission capacity of next time.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.55 no.7
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• pp.273-280
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• 2006

This paper suggests the method that forecasts Dynamic Line Rating (DLR). Thermal Overload Risk Probability (TORP) of the next time is forecasted based on the present weather conditions and DLR value by Monte Carlo Simulation (MCS). To model weather elements of transmission line for MCS process, this paper will propose the use of statistical weather models that time series is applied. Also, through the case study, it is confirmed that the forecasted TORP can be utilized as a criterion that decides DLR of next time. In short, proposed method may be used usefully to keep security and reliability of transmission line by forecasting transmission capacity of the next time.

• Journal of Information Processing Systems
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• v.15 no.4
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• pp.724-736
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• 2019

Dynamic thermal rating technology can effectively improve the thermal load capacity of transmission lines. However, its availability is limited by the quantity and high cost of the hardware facilities. This paper proposes a new dynamic thermal rating technology based on global/regional assimilation and prediction system (GRAPES) and geographic information system (GIS). The paper will also explore the method of obtaining any point meteorological data along the transmission line by using GRAPES and GIS, and provide the strategy of extracting and decoding meteorological data. In this paper, the accuracy of numerical weather prediction was verified from the perspective of time and space. Also, the 750-kV transmission line in Shaanxi Province is considered as an example to analyze. The results of the study indicate that dynamic thermal rating based on GRAPES and GIS can fully excavate the line power potential without additional cost on hardware, which saves a lot of investment.

• 한국방재학회:학술대회논문집
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• 2008.02a
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• pp.683-686
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• 2008

Flood damage has been increased due to the abnormal climate and extreme rainfall. So, quantitative and qualitative hydrologic data should be improved in oder to enhance accuracy of hydrologic forecast. However, research regarding hydrologic data have not been thorough enough. Therefore, in this study, monte carlo simulation was applied to rainfall runoff model to improve the reliability of runoff analysis and risk analysis. Rainfall-Stage-Discharge curve was developed as a consequence of MCS and it is possible to get correct rating curve for high water level.

• Korean Journal of Agricultural and Forest Meteorology
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• v.6 no.4
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• pp.242-249
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• 2004

This study was conducted to develop a forest fire occurrence model using meteorological characteristics for the practical purpose of forecasting forest fire danger. Forest fire in South Korea is highly influenced by humidity, wind speed, and temperature. To effectively forecast forest fire occurrence, we need to develop a forest fire danger rating model using weather factors associated with forest fire. Forest fore occurrence patterns were investigated statistically to develop a forest fire danger rating index using time series weather data sets collected from 8 meteorological observation centers. The data sets were for 5 years from 1997 through 2001. Development of the forest fire occurrence probability model used a logistic regression function with forest fire occurrence data and meteorological variables. An eight-province probability model by was developed. The meteorological variables that emerged as affective to forest fire occurrence are effective humidity, wind speed, and temperature. A forest fire occurrence danger rating index of through 10 was developed as a function of daily weather index (DWI).

• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.6B
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• pp.579-587
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• 2010

The mid-range streamflow forecast was performed using NWP(Numerical Weather Prediction) provided by KMA. The NWP consists of RDAPS for 48-hour forecast and GDAPS for 240-hour forecast. To enhance the accuracy of the NWP, QPM to downscale the original NWP and Quantile Mapping to adjust the systematic biases were applied to the original NWP output. The applicability of the suggested streamflow prediction system which was verified in Geum River basin. In the system, the streamflow simulation was computed through the long-term continuous SSARR model with the rainfall prediction input transform to the format required by SSARR. The RQPM of the 2-day rainfall prediction results for the period of Jan. 1~Jun. 20, 2006, showed reasonable predictability that the total RQPM precipitation amounts to 89.7% of the observed precipitation. The streamflow forecast associated with 2-day RQPM followed the observed hydrograph pattern with high accuracy even though there occurred missing forecast and false alarm in some rainfall events. However, predictability decrease in downstream station, e.g. Gyuam was found because of the difficulties in parameter calibration of rainfall-runoff model for controlled streamflow and reliability deduction of rating curve at gauge station with large cross section area. The 10-day precipitation prediction using GQPM shows significantly underestimation for the peak and total amounts, which affects streamflow prediction clearly. The improvement of GDAPS forecast using post-processing seems to have limitation and there needs efforts of stabilization or reform for the original NWP.