• 조명전기설비학회논문지
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• 제30권1호
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• pp.96-101
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• 2016

In this paper, We investigate the number of 795 transformer in the private electrical facilities and analyze the annual load factor. The results show that the annual load factor of transformer is 20.16% in manufacturing industry, education services(school) is 9.59%, retail and wholesale services is 19.68%, resort and leisure industry is 10.93%, office building is 13.10%, and apartment houses is 14.69%. Education services, resort and leisure industry are being operated with a very low annual load factor. The relatively small capacity of less than 500kVA transformer also been analyzed that is being operated with a low load factor. Therefore, In order to minimize the power loss of the transformer, it is advisable to complement the Transformer Efficiency Management system to be designed the efficiency is good transformer when the load is low. Analysis results will be used as the basis for the provision of transformer efficiency management system and be used High-efficiency transformers promotion system.

• Journal of Electrical Engineering and Technology
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• 제10권1호
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• pp.271-279
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• 2015

Induction cooking application with multiple loads need high power inverters and appropriate control techniques. This paper proposes an inverter configuration with buck-boost converter for multiple load induction cooking application with independent control of each load. It uses one half-bridge for each load. For a given dc supply of $V_{DC}$, one more $V_{DC}$ is derived using buck-boost converter giving $2V_{DC}$ as the input to each half-bridge inverter. Series resonant loads are connected between the centre point of $2V_{DC}$ and each half-bridge. The output voltage across each load is like that of a full-bridge inverter. In the proposed configuration, half of the output power is supplied to each load directly from the source and remaining half of the output power is supplied to each load through buck-boost converter. With buck-boost converter, each half-bridge inverter output power is increased to a full-bridge inverter output power level. Each half-bridge is operated with constant and same switching frequency with asymmetrical duty cycle (ADC) control technique. By ADC, output power of each load is independently controlled. This configuration also offers reduced component count. The proposed inverter configuration is simulated and experimentally verified with two loads. Simulation and experimental results are in good agreement. This configuration can be extended to multiple loads.

• 전기학회논문지
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• 제65권9호
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• pp.1626-1631
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• 2016

Generally, the low-voltage customer has been used with a linear load and nonlinear load in the 3-phase 4-wire distribution system. Linear load has usually configured the resistance and inductance, current phase is slower than the voltage phase, so power factor is low. It is required for the power factor correction device prior to the phase of the current than the voltage. The capacitor is connected in parallel to the load in order to ensure a low power factor. Power converter such as an inverter is a typical non-linear load. Non-linear load generates harmonic currents in the energy conversion process. Many electrical equipment may be adversely affected by the harmonic current. There, passive or active filter have been used to reduce these harmonics current. Passive filter consisting of inductor and capacitor generates a reactive power. According to the combination of filter inductor and capacitor, reactive power can be adjusted. In this paper, we analyzed how the combination of inductor and capacitor affects the overall power factor by simulation and measurement.

• 전기학회논문지
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• 제67권1호
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• pp.96-102
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• 2018

For efficient operating strategy of electric power system, forecasting of daily peak electric load is an important but difficult problem. Therefore a daily peak electric load forecasting system using a neural network and fuzzy system is presented in this paper. First, original peak load data is interpolated in order to overcome the shortage of data for effective prediction. Next, the prediction of peak load using these interpolated data as input is performed in parallel by a neural network predictor and a fuzzy predictor. The neural network predictor shows better performance at drastic change of peak load, while the fuzzy predictor yields better prediction results in gradual changes. Finally, the superior one of two predictors is selected by the rules based on rough sets at every prediction time. To verify the effectiveness of the proposed method, the computer simulation is performed on peak load data in 2015 provided by KPX.

• 전기전자학회논문지
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• 제22권3호
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• pp.672-679
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• 2018

Recently, microgrid (MG) has been proposed as one of the most critical solutions for various energy problems. For the optimal and economic operation of MGs, it is very important to forecast the load profile. However, it is not easy to predict the load accurately since the load in a MG is small and highly variable. In this paper, we propose an artificial neural network (ANN) based method to predict the energy use in campus buildings in short-term time series from one hour up to one week. The proposed method analyzes and extracts the features from the historical data of load and temperature to generate the prediction of future energy consumption in the building based on sparsified K-means. To evaluate the performance of the proposed approach, historical load data in hourly resolution collected from the campus buildings were used. The experimental results show that the proposed approach outperforms the conventional forecasting methods.

• Journal of Electrical Engineering and Technology
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• 제6권3호
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• pp.293-301
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• 2011

This paper presents a novel theoretical method based on power analysis to obtain voltage reference values for an inverter-based compensator. This type of compensator, which is installed in parallel with the load, is usually referred to as the active filter. The proposed method is tailored to design the compensator in such a way that it can simultaneously balance the asymmetric load, as well as correct the power factor of the supply side. For clarity, a static compensator is first considered and a recursive algorithm is utilized to calculate the reactance values. The algorithm is then extended to calculate voltage reference values when the compensator is inverter based. It is evident that the compensator would be asymmetric since the load is unbalanced. The salient feature associated with the proposed method is that the circuit representation of system load is not required and that the load is recognized just by its active and reactive consumptions. Hence, the type and connection of load do not matter. The validity and performance of the new approach are analyzed via a numerical example, and the obtained results are thoroughly discussed.

• 전기학회논문지
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• 제62권2호
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• pp.149-154
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• 2013

Short term load forecasting for electric power demand is essential for stable power system operation and efficient power market operation. High accuracy of the short term load forecasting can keep the power system more stable and save the power market operation cost. We propose an optimal coefficient selection method for exponential smoothing model in short term load forecasting on weekdays. In order to find the optimal coefficient of exponential smoothing model, load forecasting errors are minimized for actual electric load demand data of last three years. The proposed method are verified by case studies for last three years from 2009 to 2011. The results of case studies show that the average percentage errors of the proposed load forecasting method are improved comparing with errors of the previous methods.

• 조명전기설비학회논문지
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• 제28권1호
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• pp.50-56
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• 2014

Accurate midterm load forecasting is essential to preventive maintenance programs and reliable demand supply programs. This paper describes a midterm load forecasting method using autoregressive integrated moving average (ARIMA) model which has been widely used in time series forecasting due to its accuracy and predictability. The various ARIMA models are examined in order to find the optimal model having minimum error of the midterm load forecasting. The proposed method is applied to forecast 104-week load pattern using the historical data in Korea. The effectiveness of the proposed method is evaluated by forecasting 104-week load from 2011 to 2012 by using historical data from 2002 to 2010.

• 전기학회논문지
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• 제61권12호
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• pp.1765-1773
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• 2012

The accuracy of load forecast is very important from the viewpoint of economical power system operation. In general, the weekdays' load demand pattern has the continuous time series characteristics. Therefore, the conventional methods expose stable performance for weekdays. In case of special days or weekends, the load demand pattern has the discontinuous time series characteristics, so forecasting error is relatively high. Especially, weekdays near the thanksgiving day and lunar new year's day have the mixed load profile characteristics of both weekdays and weekends. Therefore, it is difficult to forecast these days by using the existing algorithms. In this study, a new load forecasting method is proposed in order to enhance the accuracy of the forecast result considering the characteristics of weekdays and weekends. The proposed method was tested with these days during last decades, which shows that the suggested method considerably improves the accuracy of the load forecast results.

• 전기학회논문지
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• 제65권7호
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• pp.1144-1150
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• 2016

Short-term load forecasting is essential to the electricity pricing and stable power system operations. The conventional weekday 24-hour load forecasting algorithms consider the temperature model to forecast maximum load and minimum load. But 24-hour load pattern forecasting models do not consider temperature effects, because hourly temperature forecasts were not present until the latest date. Recently, 3 hour temperature forecast is announced, therefore hourly temperature forecasts can be produced by mathematical techniques such as various interpolation methods. In this paper, a new 24-hour load pattern forecasting method is proposed by using similar day search considering the hourly temperature. The proposed method searches similar day input data based on the anomalous weather features such as continuous temperature drop or rise, which can enhance 24-hour load pattern forecasting performance, because it uses the past days having similar hourly temperature features as input data. In order to verify the effectiveness of the proposed method, it was applied to the case study. The case study results show high accuracy of 24-hour load pattern forecasting.