• The Transactions of the Korean Institute of Electrical Engineers A
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• v.50 no.4
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• pp.172-179
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• 2001

A component based method out of load modeling is to aggregate component load model according to the composition rate of each component load at load bus based on the circuit theory. But the most of component loads respond complex nonlinear characteristics respect to voltage and frequency variation due to the control techniques and semiconductor elements applied to component load. It needs to verify this approach through actual experiment of the aggregation of component load even if it can be down. To identify this aggregation method well known, this paper is proposed the classifying method of component load characteristics for component loads to group by quantitative analysis. The component load characteristics were divided into several types by KSOM (kohonen self organizing map), which can classify multi-dimension vector, component load pattern, into two-dimension vector. Some ambiguous cases happened from KSOM were classified by the proposed closing degree.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.52 no.3
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• pp.179-182
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• 2003

Component-based method for load model not only should include the performances of the load components, but also should take into consideration the core losses of transformers, the line losses and the capacitor banks. Especially, capacitor bank affects the accuracy of reactive load model in load modeling. But it is difficult to identify actual reactive powers of capacitor banks in power system for load modeling. This research improves the component-based modeling method including uncertain capacitor bank. The proposed method is hybrid technique, which adds the measurement-based method to the existing component-based method for reliable information of capacitor band. The results of case studies were presented to verify the validity of the proposed method.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.51 no.2
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• pp.45-52
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• 2002

Load modeling is classified into two methods according to approaching method, so called the measurement and component-based method. The measurement method is to model the load characteristics measured directly at substations and feeders. But it is difficult to measure continuously load characteristics from naturally occurring. system variation. The component-based method consists of the fellowing process; component load modeling, composition rate estimation and aggregation of component loads, etc. In this paper, the characteristic experiment of component loads was performed to obtain data for the component load modeling as the component-based method. At first, representative component loads were selected by the proposed method considering the accuracy of load modeling and the performance possibility of component load experiment in the laboratory. Also an algorithm was Proposed to identify the reliability of data obtained from the component load characteristic experiments. In addition, the results were presented as the case studies.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.13 no.4
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• pp.96-101
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• 1999

Load model is very important to improve accuracy of stability analysis and load flow study in power systems. A power system bus is composed by various loads, and loads have different power consumption due to voltage/frequency changing. Thus the effect of voltage/frequency changing must he considered to load mxleling. In this research, ANN was used to construct component load moddel for more accurate load mxleling. Typical residential load was selected, and characteristics exrerimented on voltage/frequency changing. Acquired data used to construct the component ANN model, and aggregation method of component load model was presented based on component load model and composition rate. Furthennore, it's transfomlation method to the mathematical load model to he used at the traditional power system analysis soft wares was also presented.sented.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.62 no.2
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• pp.65-71
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• 2013

Nonlinear loads with harmonics exist in an actual power system where harmonic currents make voltage distortion. The sum of reactive power measured at individual load is different from the measured reactive power at a bus in a power system with linear and non-linear loads. In this study, ANN(artificial neural network) load modeling technique with consideration of harmonics is introduced for more accurate component load modeling and an impact coefficient is proposed for aggregation of component loads. Results of this research show more accurate load modeling method. Since precise data for power system analysis can be acquired, the proposed method will be used for power system planning and maintenance.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.61 no.4
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• pp.168-173
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• 2012

The aggregation of group loads, which consists of the linear and the non-linear systems, yields the error involved in the reactive power aggregation, which is greater than the active power aggregation in the component based load modeling. Each individual reactive power in a group load affects the aggregated load different from composition rate. This paper proposes a new method that determines the degree of impacts by adjusting the coefficient of weight factors of each load using the least squares error method. The effectiveness of proposed algorithm is demonstrated by simulating three aggregation cases.

• 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.

• Journal of the Korean Data and Information Science Society
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• v.6 no.2
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• pp.1-7
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• 1995

This paper considers the reliability estimation of a two-component shared-load system based on Freund model. Maximum likelihood estimator, order restricted maximum likelihood estimator and uniformly minimum variance unbiased estimator of the reliability function for the system are obtained. Performance of three estimators for moderate sample sizes is studied by simulation.

• Journal of the Society of Naval Architects of Korea
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• v.61 no.3
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• pp.200-207
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• 2024

A multi-component force sensor has been developed to measure force and moment components in high-speed flow media for submerged models. The size of the test model is determined based on the Reynolds number of the model at the test speed and expected blockage effect. A two-component force sensor unit has been created by assembling pairs of column elements arranged symmetrically under an eccentric load. The six-component force sensor is constructed with symmetric arrangements of two-component force sensor units in a rectangular plane. The signals generated from the strain gauges attached to the surface of the elements can be converted into force signals. The performance of the waterproof six-component force sensor has been evaluated through calibration. A simplified interference decomposition procedure has been introduced to increase the accuracy of measurement.

• Journal of Internet Computing and Services
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• v.11 no.2
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• pp.31-40
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• 2010

In this paper, we propose a service component based on active model for supporting a variety of u-healthcare application services. It implemented that component as a classification of function for developing healthcare application services. Especially we focus on the adaptive information service in integrated environment using a distributed object technologies of the various healthcare home service based on distributed object group framework. And we shows the service component applying to Healthcare application services such as healthcare home monitoring, mobile monitoring and web based monitoring. Also, we show the performance evaluation results such as response time, system load and network load.