• Journal of Korean Society of Disaster and Security
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• v.14 no.2
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• pp.35-42
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• 2021

In order to produce electric vehicle demand forecasting information, which is an important element of the plan to expand charging facilities for electric vehicles, a model for predicting electric vehicle demand was proposed using Exponential Smoothing. In order to establish input data for the model, the monthly power demand of cities and counties was applied as independent variables, monthly electric vehicle charging stations, monthly electric vehicle charging stations, and monthly electric vehicle registration data. To verify the accuracy of the electric vehicle power demand prediction model, we compare the results of the statistical methods Exponential Smoothing (ETS) and ARIMA models with error rates of 12% and 21%, confirming that the ETS presented in this paper is 9% more accurate as electric vehicle power demand prediction models. It is expected that it will be used in terms of operation and management from planning to install charging stations for electric vehicles using this model in the future.

• Journal of the Korean Statistical Society
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• v.30 no.4
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• pp.645-660
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• 2001

In this paper we propose a goodness-of-fit test statistic for testing the (null) parametric model versus the (alternative) nonparametric model. Most of existing nonparametric test statistics are based on the residuals which are obtained by regressing the data to a parametric model. Our test is based on the bootstrap estimator of the probability that the smoothing parameter estimator is infinite when fitting residuals to cubic smoothing spline. Power performance of this test is investigated and is compared with many other tests. Illustrative examples based on real data sets are given.

• Journal of Electrical Engineering and Technology
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• v.12 no.6
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• pp.2099-2105
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• 2017

In a power system that has a high wind penetration, the output power fluctuation of a large-scale wind turbine generator (WTG) caused by the varying wind speed increases the maximum frequency deviation, which is an important metric to assess the quality of electricity, because of the reduced system inertia. This paper proposes a stable power-smoothing scheme of a doubly-fed induction generator (DFIG) that can suppress the maximum frequency deviation, particularly for a power system with a high wind penetration. To do this, the proposed scheme employs an additional control loop relying on the system frequency deviation that operates in combination with the maximum power point tracking control loop. To improve the power-smoothing capability while guaranteeing the stable operation of a DFIG, the gain of the additional loop is modified with the rotor speed and frequency deviation. The gain is set to be high if the rotor speed and/or frequency deviation is large. The simulation results based on the IEEE 14-bus system demonstrate that the proposed scheme significantly lessens the output power fluctuation of a WTG under various scenarios by modifying the gain with the rotor speed and frequency deviation, and thereby it can regulate the frequency deviation within a narrow range.

• Proceedings of the KIEE Conference
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• 2011.07a
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• pp.328-329
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• 2011

To promote the introduction of wind power conversion systems, which have an unstable power output, the adoption of energy storage system is being considered to mitigate variations of wind power output. This paper presents the results of analyzed data obtained by the wind power output smoothing tests.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.45 no.1
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• pp.78-85
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• 2008

Most researches for power management have focused on increasing the utilization of system performance by scaling operating frequency or operating voltage. If operating frequency is changed frequently, it reduces the real system performance. To reduce power consumption, alternative approaches use the limited number of operating frequencies or set the smoothing frequencies during execution to increase the system performance, but they are not suitable for real time applications. To reduce power consumption and increase system performance for real time applications, this paper proposes a new power-aware schedule method by allocating operating frequencies and by setting smoothing frequencies. The algorithm predicts so that frequencies with continuous interval are mapped into discrete operating frequencies. The frequency smoothing reduces overheads of systems caused by changing operating frequencies frequently as well as power consumption caused by the frequency mismatch at a wide frequency interval. The simulation results show that the proposed algorithm reduces the power consumption up to 40% at maximum and 15% on average compared to the CC RT-DVS.

• The Transactions of the Korean Institute of Power Electronics
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• v.9 no.4
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• pp.381-389
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• 2004

In this paper, it is suggested that instantaneous compensation PWM control for inverter without the smoothing capacitor Therefore, this inverter system has several advantages. It has small volume and low price to manufacture, decrease trouble rate of inverter, and has power factor correction effect because huge smoothing capacitor-less. And it has compact size control circuit to use analog integrator device. It could make the smoothing capacitor-less inverter for air-blower motor by using the instantaneous compensation PWM controller. This inverter system has small volume and value compare with the conventional VVVF control inverter.

• 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 Electrical Engineering and Technology
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• v.10 no.1
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• pp.146-154
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• 2015

This paper proposes a control method for maintaining the energy level for a supercapacitor energy storage system coupled with a wind generator to stabilize wind power output. Although wind power is green and clean energy source, disadvantage of the renewable energy output power is fluctuation. In order to mitigate the fluctuating output power, supercapacitor energy storage system (SCESS) and wind power simulator is developed. A remaining energy supercapacitor (RESC) control is introduced and analyzed to smooth for short-term fluctuating power and maintain the supercapacitor voltage within the designed operating range in the steady as well as transient state. When the average and fluctuating component of power increases instantaneously, the RESC compensates fluctuating power and the variation of fluctuating power is reduced 100% to 30% at 5kW power. Furthermore, supercapacitor voltage is maintained within the operating voltage range and near 50% of total energy. Feasibility of SCESS with RESC control is verified through simulation and experiment.

• The Proceedings of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.6 no.3
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• pp.43-49
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• 1992

Forecasting of electric power demand has been a basic element for electric power system operation and system development, and it's accuracy has very strong influence on reliability and economical efficience of power supply. So, in this paper, long―term maximum electric power demand has been forecasted by using the triple exponential smoothing method initiated R.G.Brown. It has been regarded this method as high accuracy and operational convenience. The smoothing function is a liner combination of all past observations and the weight given to previous observations decreases geometrically with age.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.9
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• pp.1695-1699
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• 2009

This paper analyzed the characteristics of the demand of electric power in Jeju by year, day. For this analysis, this research used the correlation between the changes in the temperature and the demand of electric power in summer, and cleaned the data of the characteristics of the temperatures, using the coefficient of correlation as the standard. And it proposed the algorithm of forecasting the short-term electric power demand in Jeju, Therefore, in the case of summer, the data by each cleaned temperature section were used. Based on the data, this paper forecasted the short-term electric power demand in the exponential smoothing method. Through the forecast of the electric power demand, this paper verified the excellence of the proposed technique by comparing with the monthly report of Jeju power system operation result made by Korea Power Exchange-Jeju.