• Journal of Korean Society on Water Environment
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• v.35 no.6
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• pp.520-538
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• 2019

In this study, it was proposed that a method of setting the target water quality for TOC using the watershed model and the load duration curves to manage non-biodegradable organics in the total water load management system. To simulate runoff and water quality of the watershed, the HSPF model is used which is appropriate for urban and rural areas. Additionally, the load duration curve is used to reflect the variable water quality correlated with various river flow rates in preparing the TMDL plans in the U.S. First, the model was constructed by inputting the loads calculated from the pollutant sources in 2015. After the calibration and verification process, the water quality by flow conditions was analyzed from the BOD and TOC simulation results. When the BOD achieved the target water quality by inputting the target year loads for 2020, the median and average values of TOC were proposed for the target water quality. The provisional method of TOC target water quality for the management of non-biodegradable organics, which is one of the challenges of the total water load management system, was considered. In the future, it is expected to be used as basic data for the conversion of BOD into TOC in the total water load management system.

• Journal of Electrical Engineering and Technology
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• v.7 no.5
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• pp.653-658
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• 2012

Load modeling has a significant influence on power system analysis and control. In recent years, measurement-based load modeling has been widely practiced. In the load modeling algorithm, the model structure is determined and the parameters of the established model are estimated. For parameter estimation, least-squares optimization method is applied. The model parameters are estimated so that the error between the measured values and the predicted values is to be minimized. By introducing sliding window concept, on-line load modeling method can be performed which reflects the dynamic behaviors of loads in real-time. For the purpose of data acquisition, the measurement system including PMU is implemented in university level. In this paper, case studies are performed using real PMU data from Korea Univ. and Seoul National University of Science and Technology. The performances of modeling real and reactive power behaviors using exponential and ZIP load model are evaluated.

• Structural Engineering and Mechanics
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• v.59 no.4
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• pp.703-718
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• 2016

Masonry arch bridges present a large segment of Iranian railway bridge stock. The ever increasing trend in traffic requires constant health monitoring of such structures to determine their load carrying capacity and life expectancy. In this respect, the performance of one of the oldest masonry arch bridges of Iranian railway network is assessed through field tests. Having a total of 11 sensors mounted on the bridge, dynamic tests are carried out on the bridge to study the response of bridge to test train, which is consist of two 6-axle locomotives and two 4-axle freight wagons. Finite element model of the bridge is developed and calibrated by comparing experimental and analytical mid-span deflection, and verified by comparing experimental and analytical natural frequencies. Analytical model is then used to assess the possibility of increasing the allowable axle load of the bridge to 25 tons. Fatigue life expectancy of the bridge is also assessed in permissible limit state. Results of F.E. model suggest an adequacy factor of 3.57 for an axle load of 25 tons. Remaining fatigue life of Veresk is also calculated and shown that a 0.2% decrease will be experienced, if the axle load is increased from 20 tons to 25 tons.

• Proceedings of the KIEE Conference
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• 2003.11c
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• pp.588-591
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• 2003

This paper presents a new short-term load forecasting system using data mining. Since the electric load has very different pattern according to the day, it definitely gives rise to the forecasting error if only one forecasting model is used. Thus, to resolve this problem, the fuzzy model-based classifier and predictor are proposed for the forecasting of the hourly electric load. The proposed classifier is the multi-input and multi-output fuzzy system of which the consequent part is composed of the Bayesian classifier. The proposed classifier attempts to categorize the input electric load into Monday, Tuesday$\sim$Friday, Saturday, and Sunday electric load, Then, we construct the Takagi-Sugeno (T-S) fuzzy model-based predictor for each class. The parameter identification problem is converted into the generalized eigenvalue problem (GEVP) by formulating the linear matrix inequalities (LMIs). Finally, to show the feasibility of the proposed method, this paper provides the short-term load forecasting example.

• Steel and Composite Structures
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• v.8 no.6
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• pp.441-455
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• 2008

This paper presents an effective hysteretic model for the prediction and evaluation of steel reinforced concrete member seismic performance. This model adopts the load-deformation relationship acquired from monotonic load tests and incorporates the double-pivot behavior of composite members subjected to cyclic loads. Deterioration in member stiffness was accounted in the analytical model. The composite member performance assessment control parameters were calibrated from the test results. Comparisons between the cyclic load test results and analytical model validated the proposed method's effectiveness.

• Proceedings of the Korea Concrete Institute Conference
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• 2001.11a
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• pp.865-870
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• 2001

An effective live load model for analyzing probable maximum live load effects in longitudinal direction such as moment and shear was developed. The main procedure of this live load model is composed of two parts. Firstly, determination of the appropriate influence lines, and secondly, application of the characteristics of vehicles and traffic patterns. Through this procedure, probabilistic distributions of maximum probable load effects are deduced in the form of probability density function (PDF) or cumulative density function (CDF). The proposed live load model is not limited by bridge types(number of spans or girders) and can consider local or global deterioration of bridges in the analysis. Besides, load effects can be determined at any section without restrictions.

• Proceedings of the KIEE Conference
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• 2005.07a
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• pp.184-186
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• 2005

Increasement of power demand rapid industrial growth has led the expansion of power system, and it caused construction of large power transmission line(like 765kV T/L) and substation. If there are T/L faults (route contingency etc), it lead to the large scale black out in SEOUL AREA (the center of load). To minimize damage which caused by the large scale black out, KEPCO selects the method of load shadding. In this work, instead of general method of load shadding, We study the application of UVLS model to decrease the load shadding in SEOUL AREA. The study result of using the UVLS model showed that the amont of load shadding can be decreased about 400 MW compare to the existing load shadding system.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.12
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• pp.2215-2220
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• 2011

The accurate short-term load forecasting is essential for the efficient power system operation and the system marginal price decision of the electricity market. So far, errors of load forecasting for Chuseok Holiday are very big compared with forecasting errors for the other special days. In order to improve the accuracy of load forecasting for Chuseok Holiday, selection of input data, the daily normalized load patterns and load forecasting model are investigated. The efficient data selection and daily normalized load pattern based on fuzzy linear regression model is proposed. The proposed load forecasting method for Chuseok Holiday is tested in recent 5 years from 2006 to 2010, and improved the accuracy of the load forecasting compared with the former research.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.50 no.4
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• pp.172-179
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• 2001

A component based method out of load modeling is to aggregate component load model according to the composition rate of each component load at load bus based on the circuit theory. But the most of component loads respond complex nonlinear characteristics respect to voltage and frequency variation due to the control techniques and semiconductor elements applied to component load. It needs to verify this approach through actual experiment of the aggregation of component load even if it can be down. To identify this aggregation method well known, this paper is proposed the classifying method of component load characteristics for component loads to group by quantitative analysis. The component load characteristics were divided into several types by KSOM (kohonen self organizing map), which can classify multi-dimension vector, component load pattern, into two-dimension vector. Some ambiguous cases happened from KSOM were classified by the proposed closing degree.

• Journal of the Institute of Convergence Signal Processing
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• v.2 no.3
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• pp.73-78
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• 2001

BP neural network model and multiple-regression model were composed for forecasting the special-days load. Special-days load was forecasted using that neural network model made use of pattern conversion ratio and multiple-regression made use of weekday-change ratio. This methods identified the suitable as that special-days load of short and long term was forecasted with the weekly average percentage error of 1∼2[%] in the weekly peak load forecasting model using pattern conversion ratio. But this methods were hard with special-days load forecasting of summertime. therefore it was forecasted with the multiple-regression models. This models were used to the weekday-change ratio, and the temperature-humidity and discomfort-index as explanatory variable. This methods identified the suitable as that compared forecasting result of weekday load with forecasting result of special-days load because months average percentage error was alike. And, the fit of the presented forecast models using statistical tests had been proved. Big difficult problem of peak load forecasting had been solved that because identified the fit of the methods of special-days load forecasting in the paper presented.