• KEPCO Journal on Electric Power and Energy
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• v.7 no.1
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• pp.115-123
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• 2021

Through machine learning-based load prediction, it is possible to prevent excessive power generation or unnecessary economic investment by estimating the appropriate amount of facility investment in consideration of the load that will increase in the future or providing basic data for policy establishment to distribute the maximum load. However, in order to secure the reliability of the developed load prediction model in the field, the performance comparison verification between the distribution line load prediction models must be preceded, but a comparative performance verification system between the distribution line load prediction models has not yet been established. As a result, it is not possible to accurately determine the performance excellence of the load prediction model because it is not possible to easily determine the likelihood between the load prediction models. In this paper, we developed a reliability verification system for load prediction models including a method of comparing and verifying the performance reliability between machine learning-based load prediction models that were not previously considered, verification process, and verification result visualization methods. Through the developed load prediction model reliability verification system, the objectivity of the load prediction model performance verification can be improved, and the field application utilization of an excellent load prediction model can be increased.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.35 no.1
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• pp.24-31
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• 2012

This study is designed to predict the overall electric power load, to apply the method of time sharing and to reduce simultaneous load factor of electric power when authorized by user entering demand plans and using schedules into the user's interface for a certain period of time. This is about smart grid, which reduces electric power load through simultaneous load factor of electric power reduction system supervision agent. Also, this study has the following characteristics. First, it is the user interface which enables authorized users to enter and send/receive such data as demand plan and using schedule for a certain period of time. Second, it is the database server, which collects, classifies, analyzes, saves and manages demand forecast data for a certain period of time. Third, is the simultaneous load factor of electric power control agent, which controls usage of electric power by getting control signal, which is intended to reduce the simultaneous load factor of electric power by the use of the time sharing control system, form the user interface, which also integrate and compare the data which were gained from the interface and the demand forecast data of the certain period of time.

• Journal of Electrical Engineering and Technology
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• v.4 no.4
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• pp.476-484
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• 2009

This paper deals with improvements to the special protection schemes (SPS) which have been applied to the low probability and high impact contingencies in the Korea Electric Power Corporation (KEPCO) system since 2004. Among them, the SPS for voltage instability in the Seoul metropolitan area is considered in this paper, and is a form of event-based undervoltage load shedding with a single-step scheme. Simulation results based upon a recent event that occurred on 765kV lines show that the current setting values of the SPS have to be revised and enhanced. In addition, by applying response-based multi-step undervoltage load shedding (UVLS) schemes to severe contingencies in the system, more effective results than those of the existing single-step SPS can be obtained. Centralized and distributed UVLS schemes are considered in the simulation. ULTC-based load recovery models and over excitation limiters (OXL) for the KEPCO system are also included in the long-term voltage instability studies.

• KEPCO Journal on Electric Power and Energy
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• v.1 no.1
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• pp.61-65
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• 2015

For effective distribution planning and operation, we need a reliable estimation of operation capacity. But it is difficult to ensure reliability due to the low accuracy of section load data, which is used as a basis in estimating the operation capacity. This paper discusses how to improve the accuracy of section load data by analyzing the existing method of estimating the section load, using statistical techniques to adjust the acquired data, and using the section load estimation algorithm to estimate the section load based on the adjusted data.

• KEPCO Journal on Electric Power and Energy
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• v.7 no.1
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• pp.171-177
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• 2021

In terms of distribution planning, accurate electric load prediction is one of the most important factors. The future load prediction has manually been performed by calculating the maximum electric load considering loads transfer/switching and multiplying it with the load increase rate. In here, the risk of human error is inherent and thus an automated maximum electric load forecasting system is required. Although there are many existing methods and techniques to predict future electric loads, such as regression analysis, many of them have limitations in reflecting the nonlinear characteristics of the electric load and the complexity due to Photovoltaics (PVs), Electric Vehicles (EVs), and etc. This study, therefore, proposes a method of predicting future electric loads on distribution lines by using Machine Learning (ML) method that can reflect the characteristics of these nonlinearities. In addition, predictive models were developed based on actual data collected at KEPCO's existing distribution lines and the adequacy of developed models was verified as well. Also, as the distribution planning has a direct bearing on the investment, and amount of investment has a direct bearing on the maximum electric load, various baseline such as maximum, lowest, median value that can assesses the adequacy and accuracy of proposed ML based electric load prediction methods were suggested.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.2
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• pp.253-256
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• 2016

In this paper, the power system with electric vehicles is analyzed considering the mobility and diffusion rate of electric vehicles in the smart grid environment. In the previous studies, load modeling and load composition rates have been researched and the results are applied to develop a new load model to explain the mobility of electric vehicles which could affect on the power system status such as power flow and stability. The results would be utilized to research and develop power system analysis methods considering movable charging characteristics of electric vehicles including movable discharging characteristics which could be affected by the diffusion progress of electric vehicles.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1384-1386
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• 2005

This paper focuses on development of load test simulator of a steam turbine-generator in a nuclear power plant. When load is taken off from electrical power network, it is very difficult to effectively control the steam flow to turbine of the nuclear turbine-generator, because of disturbances, such as electrical load and network unbalance on electrical network. Up to the present time, the conventional control system has been used for the load control on nuclear steam generator, owing to the easy control algorithms and the advantage which have been proven on the nuclear power plant. However, since there are problems with stability control during low power and start-up, only a highly experienced operator can operate during those procedures. Also, a great deal of time and an expensive simulator is needed for the training of an operator. The KEPRI is developed simulator for 600MW nuclear power plant to take a test of generator load rejection, throttle valve, and turbine load control. Total load test is implemented before start up.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.2
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• pp.315-323
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• 2015

Nowadays, it is expected that mobility loads such as electric railways and electric vehicles will be penetrated gradually and affect on the power system stability by their load characteristics. Various researches have been carried out about electric vehicles for the recent decade though the load of electric railway could be forecasted because of the specified path and timetable, is a field with a long historic background. Some precise 5th polynomial equations are required to analyze the power system stability considering mobility load to be increased in the immediate future while the electric railway dispatching simulator uses load models with constant power and constant impedance for the system analysis. In this paper, seasonal urban railway load models are established as the form of 5th polynomial equations and substation load modeling methods are proposed merging railway station load models and general load models. Additionally, load management effects by the load modeling are confirmed through the case studies, in which seasonal load models are developed for Seoul Subway Line No. 2, Gyeongui Line and Airport Railroad and the substation load change is analyzed according to the railway load change.

• Proceedings of the KIEE Conference
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• 1999.07c
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• pp.1478-1480
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• 1999

The accurate analysis of power system requires detailed load model. There are two basic approaches in modeling the load characteristics. One is to directly measure the voltage and frequency sensitivity of the load P and Q at substations and feeders. The other is to build up a composite load model from each load component. Each of these methods has advantages and disadvantages. This paper presents load composition for KEPCO's power system to develop load models by the component-based load modeling.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.63 no.9
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• pp.1186-1191
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• 2014

Weekly and monthly electric load forecasting are essential for the generator maintenance plan and the systematic operation of the electric power reserve. This paper proposes the weekly maximum electric load forecasting model for 104 weeks with the multiple regression model. Input variables of the multiple regression model are temperatures and GDP that are highly correlated with electric loads. The weekly variable is added as input variable to improve the accuracy of electric load forecasting. Test results show that the proposed algorithm improves the accuracy of electric load forecasting over the seasonal autoregressive integrated moving average model. We expect that the proposed algorithm can contribute to the systematic operation of the power system by improving the accuracy of the electric load forecasting.