• Journal of Korean Society of Industrial and Systems Engineering
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• v.39 no.1
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• pp.25-30
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• 2016

It is critical to forecast the maximum daily and monthly demand for power with as little error as possible for our industry and national economy. In general, long-term forecasting of power demand has been studied from both the consumer's perspective and an econometrics model in the form of a generalized linear model with predictors. Time series techniques are used for short-term forecasting with no predictors as predictors must be predicted prior to forecasting response variables and containing estimation errors during this process is inevitable. In previous researches, seasonal exponential smoothing method, SARMA (Seasonal Auto Regressive Moving Average) with consideration to weekly pattern Neuron-Fuzzy model, SVR (Support Vector Regression) model with predictors explored through machine learning, and K-means clustering technique in the various approaches have been applied to short-term power supply forecasting. In this paper, SARMA and intervention model are fitted to forecast the maximum power load daily, weekly, and monthly by using the empirical data from 2011 through 2013. $ARMA(2,\;1,\;2)(1,\;1,\;1)_7$ and $ARMA(0,\;1,\;1)(1,\;1,\;0)_{12}$ are fitted respectively to the daily and monthly power demand, but the weekly power demand is not fitted by AREA because of unit root series. In our fitted intervention model, the factors of long holidays, summer and winter are significant in the form of indicator function. The SARMA with MAPE (Mean Absolute Percentage Error) of 2.45% and intervention model with MAPE of 2.44% are more efficient than the present seasonal exponential smoothing with MAPE of about 4%. Although the dynamic repression model with the predictors of humidity, temperature, and seasonal dummies was applied to foretaste the daily power demand, it lead to a high MAPE of 3.5% even though it has estimation error of predictors.

• Transactions of the Korean Society of Automotive Engineers
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• v.17 no.2
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• pp.57-66
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• 2009

In this paper, the steering characteristics of tracked vehicles are studied for the improvement of steering performance. The important design factor of military vehicles is high mobility. It is influenced by weight of a vehicle, engine capacity, power-train, and steering system. The military vehicle, which is equipped with caterpillar, has unique steering characteristics and is quite different from that of a wheeled vehicle. The steering of tracked vehicles is operated in the power pack due to different speeds of both sprockets. Under cornering conditions, power split and power regeneration are happened in the power pack. In case of power regeneration, power is transferred outside track after adding engine power and power inputted inside track from the ground. However, excessive power regeneration is transferred in the power pack. It damages mechanical elements. Therefore, it is necessary to analyze the steering system and check mentioned problem above. In this study, the detailed dynamic model of steering system is presented, which includes slippage between track and roadwheel, inertia force, and inertia moment. Finally, our model is compared with the Kitano model and we verified the validity of the model.

• Journal of Electrical Engineering and Technology
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• v.13 no.6
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• pp.2168-2177
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• 2018

The unavoidable forecast error of wind power is one of the biggest obstacles for wind farms to participate in day-ahead electricity market. To mitigate the deviation from forecast, installation of energy storage system (ESS) is considered. An accurate model of wind power forecast error is fundamental for ESS sizing. However, previous study shows that the error distribution has variable kurtosis and fat tails, and insufficient measurement data of wind farms would add to the difficulty of modeling. This paper presents a comprehensive way that makes the use of mixed skewness model (MSM) and copula theory to give a better approximation for the distribution of forecast error, and it remains valid even if the dataset is not so well documented. The model is then used to optimize the ESS power and capacity aiming to pay the minimal extra cost. Results show the effectiveness of the new model for finding the optimal size of ESS and increasing the economic benefit.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.52 no.7
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• pp.381-386
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• 2003

To provide a specified power quality under electric market system is becoming an important issue for customers and utility company. However, there is no realistic infra-structure to design a power system for the specified power quality. Present electric market is operating under the economic point of view. The low power price could be attractive, but the effect of low price could result the lower power quality for the long time and threat power system security. This paper presents a model which conceptualize the dynamic power quality control mechanism to minimize total cost of a society which is affected electric power quality. This model aims to produce a basic infra-structure to balance cost and quality under the electric market system.

• Journal of Power Electronics
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• v.19 no.5
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• pp.1289-1302
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• 2019

In this paper, an optimal allocation method of a hybrid active power filter in an active distribution network is designed based on the differential evolution algorithm to resolve the harmonic generation problem when a distributed generation system is connected to the grid. A distributed generation system model in the calculation of power flow is established. An improved back/forward sweep algorithm and a decoupling algorithm are proposed for fundamental power flow and harmonic power flow. On this basis, a multi-objective optimization allocation model of the location and capacity of a hybrid filter in an active distribution network is built, and an optimal allocation scheme of the hybrid active power filter based on the differential evolution algorithm is proposed. To verify the effect of the harmonic suppression of the designed scheme, simulation analysis in an IEEE-33 nodes model and an experimental analysis on a test platform of a microgrid are adopted.

• The KIPS Transactions:PartA
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• v.19A no.3
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• pp.139-146
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• 2012

In the server cluster environment, one of the ways saving energy is to control server's power according to traffic conditions. This is to determine the ON/OFF state of servers according to energy usage of data center and each server. To do this, we need a way to estimate each server's energy. In this paper, we use a software-based power consumption estimation model because it is more efficient than the hardware model using power meter in terms of energy and cost. The traditional software-based power consumption estimation model has a drawback in that it doesn't know well the computing status of servers because it uses only the idle status field of CPU. Therefore it doesn't estimate consumption power effectively. In this paper, we present a CPU field based power consumption estimation model to estimate more accurate than the two traditional models (CPU/Disk/Memory utilization based power consumption estimation model and CPU idle utilization based power consumption estimation model) by using the various status fields of CPU to get the CPU status of servers and the overall status of system. We performed experiments using 2 PCs and compared the power consumption estimated by the power consumption model (software) with that measured by the power meter (hardware). The experimental results show that the traditional model has about 8-15% average error rate but our proposed model has about 2% average error rate.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.6
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• pp.1079-1085
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• 2011

To supply electrical power with high quality, the power system must be optimized in many ways such as planning, control and management. In order to optimize the power system, the analysis of the power system is necessary. The elements of the power system require an accurate model to analysis of the power system. The components of the power systems such as generators, transformers and transmission lines have been studied and researched a lot in their modeling and very sophisticated models have been proposed. However, in case of load in-depth studies on the exact model are required. In this paper, measurement-based load modeling method using real-time measured data is proposed in various methods to reflect the characteristics of the load. To prove the validity of the proposed method, PSCAD/EMTDC program is used to configure the power system and measurement data according to the various failures are used to study on load modeling.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.63 no.3
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• pp.189-194
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• 2014

Due to the increasing of power consumption, it is difficult to construct accurate prediction model for daily peak power demand. It is very important work to know power demand in next day for manager and control power system. In this research, we develop a daily peak power demand prediction method based on hybrid type composed of AR and Neuro-Fuzzy model. Using data sets between 2006 and 2010 in Korea, the proposed method has been intensively tested. As the prediction results, we confirm that the proposed method makes it possible to effective estimate daily peak power demand than conventional methods.

• Communications for Statistical Applications and Methods
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• v.23 no.4
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• pp.321-334
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• 2016

In this paper, we propose power t distribution based on t distribution. We also study the properties of and inferences for power t model in order to solve the problem of real data showing both skewness and heavy tails. The comparison of skew t and power t distributions is based on density plots, skewness and kurtosis. Note that, at the given degree of freedom, the kurtosis's range of the power t model surpasses that of the skew t model at all times. We draw inferences for two parameters of the power t distribution and four parameters of the location-scale extension of power t distribution via maximum likelihood. The Fisher information matrix derived is nonsingular on the whole parametric space; in addition we obtain the profile log-likelihood functions on two parameters. The response plots for different sample sizes provide strong evidence for the estimators' existence and unicity. An application of the power t distribution suggests that the model can be very useful for real data.

• KIEE International Transactions on Power Engineering
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• v.4A no.2
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• pp.91-99
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• 2004

This paper illustrates a tie line constrained equivalent assisting generator (TEA G) model considering forced outage rates of transmission systems for reliability evaluation of interconnected power systems. Interconnections between power systems can provide improved levels of reliability. It is expected that the TEAG model developed in this paper will prove useful in the solution to problems related to the effect of transmission system uncertainties in the reliability evaluation of interconnected power systems. The characteristics and concept of this TEAG considering transmission systems are described in detail by sample studies on a simple test system.