• Journal of Korean Institute of Industrial Engineers
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• v.28 no.1
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• pp.63-75
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• 2002

To improve the efficiency of the electric power generation, monthly maximum electric power consumptions for a next one year should be forecasted in advance and used as the fundamental input to the yearly electric power-generating master plan, which has a greatly influence upon relevant sub-plans successively. In this paper, we analyze the past 22-year hourly maximum electric load data available from KEPCO(Korea Electric Power Corporation) and select necessary data from the raw data for our model in order to reflect more recent trends and seasonal components, which hopefully result in a better forecasting model in terms of forecasted errors. After analyzing the selected data, we recommend to KEPCO the Winters' multiplicative model with decomposition and exponential smoothing technique among many candidate forecasting models and provide forecasts for the electric power consumptions and their 95% confidence intervals up to December of 1999. It turns out that the relative errors of our forecasts over the twelve actual load data are ranged between 0.1% and 6.6% and that the average relative error is only 3.3%. These results indicate that our model, which was accepted as the first statistical forecasting model for monthly maximum power consumption, is very suitable to KEPCO.

• 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.

• Journal of the Korean Society of Safety
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• v.15 no.2
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• pp.63-69
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• 2000

The constraint conditions are the stator voltage and the stator current to operate the motor in the flux weakening region. The maximum current is limited by the inverter current rating and the machine thermal rating. Given DC link voltage to control the motor in the flux weakening the maximum voltage is determined by considering PWM strategy, dead time, voltage drop of the inverter switching device, and the margin of the voltage for current forcing. In this paper, the new method to determine the available maximum voltage is derived by the quantitative method and by considering the factors of the voltage drop. The proposed method to determine the maximum voltage is very useful to improve the stability of the motor system and to enlarge the speed operation region in the flux weakening operation. Therefore the utility of the maximum voltage is increased.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.63 no.11
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• pp.1497-1502
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• 2014

Electric load forecasting is essential for stable electric power supply, efficient operation and management of power systems, and safe operation of power generation systems. The results are utilized in generator preventive maintenance planning and the systemization of power reserve management. Development and improvement of electric load forecasting model is necessary for power system maintenance and operation. This paper proposes daily maximum electric load forecasting methods for the next 4 weeks with a seasonal autoregressive integrated moving average model and an exponential smoothing model. According to the results of forecasting of daily maximum electric load forecasting for the next 4 weeks of March, April, November 2010~2012 using the constructed forecasting models, the seasonal autoregressive integrated moving average model showed an average error rate of 6,66%, 5.26%, 3.61% respectively and the exponential smoothing model showed an average error rate of 3.82%, 4.07%, 3.59% respectively.

• Journal of Auto-vehicle Safety Association
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• v.10 no.2
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• pp.36-42
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• 2018

UNECE/WP29/GRPE/EVE has recently defined that the power of a hybrid electric vehicle is the system power. Although a method for measuring the maximum power of a hybrid electric vehicle is presented by KATRI, it does not consider charging and discharging characteristics of traction batteries. This study provides a maximum power measurement method which reflects the charging and discharging characteristics of traction batteries in NOVC-HEVs (Not Off Vehicle Charging-Hybrid Electric Vehicles). Both methods are compared with regard to the output measurement results.

• Journal of the Korean Professional Engineers Association
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• v.44 no.2
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• pp.29-33
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• 2011

In recent years, maximum electric power demand has been increasing steadily. But, Electric Power Supply & Demand problem is occurring due to lack of electric power reserve ratio caused by electric power peak. For this reason, I investigated the current status of the Electric Power Supply & Demand and established Electric Power Supply & Demand and established Electric Power Supply & Demand measures. I will expect that this paper will be contributed balanced and stable Electric Power Supply & Demand management.

• Transactions of the Korean Society of Automotive Engineers
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• v.22 no.7
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• pp.135-143
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• 2014

Electrified vehicles can be classified with hybrid electric vehicles, battery electric vehicles and fuel cell electric vehicles. These vehicles have two more than energy converters which are the part of a powertrain. It is particularly difficult to estimate the power of hybrid electric vehicles due to two different energy converters with different power characteristics. Therefore, a new power concept for these vehicles is needed. The vehicle power as the new concept for solving this problem was defined in this study. The test method and the procedure were made a development in this study. Four electrified vehicles with different electric fraction were used to validate the method. Two percentage of COV was suggested as a criterion for the maximum vehicles power based on the previous studies. The repeatability of this method was within ${\pm}2$ per cent for the maximum vehicle power and within ${\pm}5$ per cent for the vehicle speed at maximum vehicle power.

• The Transactions of the Korean Institute of Power Electronics
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• v.22 no.3
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• pp.199-206
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• 2017

Maximum Power Point Tracking (MPPT) control needs to generate the maximum power of a tidal current turbine. A tidal current speed sensor is required to achieve effective generated power in a tidal current generation system. The most common methods used to achieve such power is the tip speed ratio of turbine and tidal current information. However, these methods have disadvantages, such as expensive installation of the tidal current sensor, parameter errors in turbine design, and different information according to the installed position of the tidal current sensor. This paper proposes a maximum power control scheme using perturb-and-observe (P&O) for tidal current generation system. The proposed P&O MPPT scheme can achieve the maximum power without tidal current sensors and turbine design parameters. The reliability and suitability of the proposed control scheme are proven through simulation and experiment results at the tidal current generation laboratory.

• Journal of Power Electronics
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• v.15 no.2
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• pp.487-496
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• 2015

In order to ensure that photovoltaic (PV) systems work at the maximum power point (MPP) and maximize the economic benefits, maximum power point tracking (MPPT) techniques are normally applied to these systems. One of the most widely applied MPPT methods is the incremental conductance (INC) method. However, the choice of the step size still remains controversial. This paper presents an improved variable step size INC MPPT algorithm that uses four different step sizes. This method has the advantages of INC but with the ability to validly adjust the step size to adapt to changes of the PV's power curve. The presented algorithm also simultaneously achieves increased rapidity and accuracy when compared with the conventional fixed step size INC MPPT algorithm. In addition, the theoretical derivation and specific applications of the proposed algorithm are presented here. This method is validated by simulation and experimental results.

• Journal of applied mathematics & informatics
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• v.26 no.1_2
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• pp.337-347
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• 2008

Based on social welfare maximum theory, the optimal scale of power plants entering generation power market being is researched. A static non-cooperative game model for short-term optimization of power plants with different cost is presented. And the equilibrium solutions and the total social welfare are obtained. According to principle of maximum social welfare selection, the optimization model is solved, optimal number of power plants entering the market is determined. The optimization results can not only increase the customer surplus and improve power production efficiency, but also sustain normal profits of power plants and scale economy of power production, and the waste of resource can also be avoided. At last, case results show that the proposed model is efficient.