• Journal of Electrical Engineering and Technology
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• v.5 no.2
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• pp.307-318
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• 2010

In this work, an accurate analysis of low frequency current ripple in residential fuel cell power generation systems is performed based on the proposed residential load model and its unique operation algorithm. Rather than using a constant dc voltage source, a proton exchange membrane fuel cell (PEMFC) model is implemented in this research so that a system-level analysis considering the fuel cell stack, power conditioning system (PCS), and the actual load is possible. Using the attained results, a comparative study regarding the discrepancies of low frequency current ripple between a simple resistor load and a realistic residential load is performed. The data indicate that the low frequency current ripple of the proposed residential load model is increased by more than a factor of two when compared to the low frequency current ripple of a simple resistor load under identical conditions. Theoretical analysis, simulation data, and experimental results are provided, along with a model of the load usage pattern of low frequency current ripples.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.61 no.1
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• pp.29-34
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• 2012

Due to the increasing of nonlinear loads such as converters and inverters connected to the electric power distribution system, and extensive application of harmonic generation sources with power electronic devices, disturbance of the electric power system and its influences on industries have been continuously increasing. Thus, it is difficult to construct accurate load model for active and reactive power in environments with harmonics. In this research, we develop a load modeling method based on Extreme Learning Machine(ELM) with fast learning procedure for residential loads. Using data sets acquired from various residential loads, the proposed method has been intensively tested. As the experimental results, we confirm that the proposed method makes it possible to effective estimate active and reactive powers than conventional methods.

• Proceedings of the KIEE Conference
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• 2006.07a
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• pp.212-213
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• 2006

Load models are important for improving the accuracy of stability analysis and power flow studies. Load characteristics change for different voltages and frequencies. In this research, ANN is used to construct the load model. Characteristics of some residential loads are tested under various voltage and frequency conditions. Acquired data are used to construct load models by ANN. Experiments and modeling results are presented in conclusions.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.60 no.1
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• pp.6-12
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• 2011

In this study, we proposed a residential load modeling method based on neuro-fuzzy inference system by considering of various harmonics. The developed method was implemented by using harmonic information, fundamental frequency and voltage which are essential input factors in conventional method. Thus, the proposed method makes it possible to effectively estimate load characteristics in power lines with harmonics. To show the effectiveness, the proposed method has been intensively tested with various dataset acquired under the different frequency and voltage and compared it with a conventional method based on neural networks.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.57 no.4
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• pp.362-369
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• 2008

In this study, we developed RBFN(Radial Basis Function Networks) based load modeling method with harmonic components. The developed method considers harmonic information as well as fundamental frequency and voltage considered as essential factors in conventional method. Thus, the proposed method makes it possible to effectively estimate load characteristics in power lines with harmonics. RBFN has some advantage such as simple structure and rapid computation ability compared with multi-layer perceptorn which is extensively applied for load modeling. To verify the effectiveness, the proposed method has been intensively tested with various dataset acquired under the different frequency and voltage and compared it with conventional methods such as polynomial method, MLPN and RBFN with no harmonic components.

• Proceedings of the KIEE Conference
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• 1999.07c
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• pp.1230-1232
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• 1999

In this research, ANN load model was built on results of field test using residential load, and then proposed ANN load model was applied to transient analysis. The results of this research are as follows. The first, component load modeling using ANN was implemented. The second, group load model was proposed by aggregation of component load. The third, proposed load model was applied to power system analysis. Therefore, Importance of load modeling and precise load modeling method was suggested in this paper.

• KIEE International Transactions on Power Engineering
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• v.2A no.4
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• pp.136-144
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• 2002

Load models are very important for improving the accuracy of stability analysis and load flow studies. Various loads are connected to a power bus and their characteristics of power consumption change with voltage and frequency. Thus, the effect of voltage/frequency changes must be considered in load modeling. In this work, artificial neural networks-ANNs- were used to construct the component load models for more accurate modeling. A typical residential load was selected and subjected to a test under variable voltage/frequency conditions. Acquired data were used to construct component models by ANNs. The aggregation process of separately determined load models is also presented in the paper. Furthermore, this paper proposes a method to transform a single load model constructed by the aggregation method into a mathematical load model that can be used in traditional power system analysis software.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.12
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• pp.2385-2390
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• 2009

Based on the detailed analysis of output characteristics of PV array and residential load usage pattern, a design method to calculate optimal battery capacity for stand-alone PV generation systems is proposed. And also, according to power flow Actual irradiation and temperature data are analyzed to compose a PV array simulator and also six representative home appliances are electrically modeled for load simulator, along with 24hours usage pattern. The surplus and insufficient power can be calculated from the proposed simulation platform, so that selection of an optimal battery capacity can be possible. The theoretical analysis and design process will be explained, along with informative simulation results.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.1
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• pp.16-22
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• 2016

This paper addresses the load usage scheduling in the HEMS for residential power consumers. The HEMS would lead the residential users to change their power usage, so as to minimize the cost in response to external information such as a time-varying electricity price, the outside temperature. However, there may be a consumer's inconvenience in the change of the power usage. In order to improve this, it is required to understand the pattern of load usage according to the external information. Therefore, this paper suggests a methodology to model the load usage pattern, which classifies home appliances according to external information affecting the load usage and models the usage pattern for each appliance based on a copula function representing the correlation between variables. The modeled pattern would be reflected as a constraint condition for an optimal load usage scheduling problem in HEMS. To explain an application of the methodology, a case study is performed on an electrical water heater (EWH) and an optimal load usage scheduling for EHW is performed based on the branch-and-bound method. From the case study, it is shown that the load usage pattern can contribute to an efficient power consumption.

• Fire Science and Engineering
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• v.25 no.6
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• pp.58-63
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• 2011

This study is intended to present a computational thermal model for the combustibles in a residential building. As the Performance Based Design is more popular, fire-intensity and fire-load have turned out to be very important factors for building design and can be predicted through some computational work. To predict and estimate the thermal properties of the residential combustible fire, we made some numerical models of combustibles in a residential building. In a bid to validate the estimate values, computational analysis results from numerical models were compared with real fire tests. For computational analysis, the Fire Dynamics Simulator was used with Large Eddy Simulation model for turbulence. Consequently, each heat release rate and total heat release curves were successfully estimated.