• The Transactions of The Korean Institute of Electrical Engineers
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• v.62 no.10
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• pp.1458-1464
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• 2013

This paper proposed a new real time catenary impedance estimation technique using synchronized power data from the measured data of operating vehicle and substation for catenary protective relay and fault locator setting. This paper presented estimation equation of catenary impedance using synchronized power data between substation and vehicle of AT feeding system for the performance verification of the proposed technique. Also AC feeding system is modeled through power analysis program and performance was verified through simulation according to various load changes. We verified that average 2.38%(distance equivalent 23.8 m) error appeared between the proposed estimation equation of catenary impedance and power analysis program simulation output in no connection double track system between up track and down track. Furthermore, We confirmed that estimation error is bigger depending on the increasing the distance from substation and vehicle impedance using only using vehicle current when calculating vehicle impedance in connection double track system between up track and down track. But, We confirmed that the proposed technique estimated accurately catenary impedance regardless of vehicle impedance and distance from substation.

• Transactions of The Korea Fluid Power Systems Society
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• v.8 no.4
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• pp.1-8
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• 2011

Injection molding machine is the assembly of many kinds of mechanical and fluid power part and electro-electronic control system. From in these, fluid power is a part where becomes the first core of this machine. Fluid power systems of injection molding machine are modelled and analyzed using a commercial program AMESim. The analysis model which is detailed about the parts applied a publishing catalog data. Sub system models which is divided according to functional operation are made and its analysis results shows how design parameters work on operational characteristics like displacement, pressure, flow rates at each node and so on. Total fluid power circuit model is also made and analyzed. The results made by analysis will be used design of fluid power circuit of injection molding machine.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.18 no.1
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• pp.60-66
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• 2004

In order to analyze voltage, current electrical power, harmonic and so on of electrical load equipments, electrical power analysis by real measurement rather than mathematical modeling is necessary, and plan of countermeasure for efficient management, energy frugality and accident prevention of electrical equipments using it is possible Especially, electrical power analysis by real measurement is indispensable in order to consider countermove of harmonic occurred by nonlinear load So, in this paper, we developed DSP(Digital Signal Processor) based low price date aquisition system, and verified it's ability by performing measurement and analysis of electrical power and harmonic in the real power system.

• Communications for Statistical Applications and Methods
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• v.27 no.6
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• pp.589-602
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• 2020

The development of smart grids has enabled the easy collection of a large amount of power data. There are some common patterns that make it useful to cluster power consumption patterns when analyzing s power big data. In this paper, clustering analysis is based on distance functions for time series and clustering algorithms to discover patterns for power consumption data. In clustering, we use 10 distance measures to find the clusters that consider the characteristics of time series data. A simulation study is done to compare the distance measures for clustering. Cluster validity measures are also calculated and compared such as error rate, similarity index, Dunn index and silhouette values. Real power consumption data are used for clustering, with five distance measures whose performances are better than others in the simulation.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2006.11a
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• pp.797-800
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• 2006

The power flow analysis(PFA) can be effectively used to predict structural vibration in medium-to-high frequency ranges. In this paper, vibration experiment has been performed to observe the analytical characteristics of the power flow analysis of the vibration of a plate. In the experiment, the loss factor of the plate and the input mobility at a source point have been measured. The data for the loss factor has been used as the input data to predict the vibration of the plate with PFA. The frequency response functions have been measured over the surface of the plate. The comparison between the experimental results and the predicted results for the frequency responsefunctionshasbeenperformed.

• Proceedings of the KIEE Conference
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• 2015.07a
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• pp.286-287
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• 2015

Currently, Studies on improving the reliability of power supply is becoming an important issue because of the increase in demand of the electric power system. Therefore necessity of automation in distribution system is increasing day by day. However, a measured voltage data from FRTU of distribution automation system is incorrect because of installation space limits. Therefore there is a need of system analysis method by considering the characteristics of the distribution system. For a distribution system, applying the power flow method of transmission system has some problems, as distribution is radial system and it has unbalanced load. Therefore power flow by considering the characteristics of the distribution system have been studied. Existing power flow analysis of the distribution system has different methods like direct analysis method, backward/forward sweep method, modified method of newton raphson etc. In this paper, an improved power flow analysis method based on backward/forward sweep method is proposed in order to efficiently operate the distribution automation system. The proposed method of power flow has been verified through the result of case study.

• Journal of the Korean Solar Energy Society
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• v.30 no.3
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• pp.65-70
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• 2010

As for the present wind power industry, most of the computerization for monitoring and control is based on the traditional development methodology, but it is necessary to improve SCADA system since it has a phenomenon of backlog accumulation in the applicable aspect of back-data as well as in the operational aspect in the future. Especially for a system like offshore wind power where a superintendent cannot reside, it is desirable to operate a remote control system. Therefore, it is essential to establish a monitoring system with appropriate control and monitoring inevitably premised on the integrity and independence of data. As a result, a study was carried out on the modeling of offshore wind power data-centered database. In this paper, a logical data modeling method was proposed and designed to establish the database of offshore wind power. In order for designing the logical data modeling of an offshore wind power system, this study carried out an analysis of design elements for the database of offshore wind power and described considerations and problems as well. Through a comparative analysis of the final database of the newly-designed off-shore wind power system against the existing SCADA System, this study proposed a new direction to bring about progress toward a smart wind power system, showing a possibility of a service-oriented smart wind power system, such as future prediction, hindrance-cause examination and fault analyses, through the database integrating various control signals, geographical information and data about surrounding environments.

• Korean Journal of Remote Sensing
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• v.23 no.5
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• pp.393-400
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• 2007

We present in this paper a novel power load prediction method using temporal pattern mining from AMR(Automatic Meter Reading) data. Since the power load patterns have time-varying characteristic and very different patterns according to the hour, time, day and week and so on, it gives rise to the uninformative results if only traditional data mining is used. Also, research on data mining for analyzing electric load patterns focused on cluster analysis and classification methods. However despite the usefulness of rules that include temporal dimension and the fact that the AMR data has temporal attribute, the above methods were limited in static pattern extraction and did not consider temporal attributes. Therefore, we propose a new classification method for predicting power load patterns. The main tasks include clustering method and temporal classification method. Cluster analysis is used to create load pattern classes and the representative load profiles for each class. Next, the classification method uses representative load profiles to build a classifier able to assign different load patterns to the existing classes. The proposed classification method is the Calendar-based temporal mining and it discovers electric load patterns in multiple time granularities. Lastly, we show that the proposed method used AMR data and discovered more interest patterns.

• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.594-597
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• 2007

We present in this paper a novel mid and long term power load prediction method using temporal pattern mining from AMR (Automatic Meter Reading) data. Since the power load patterns have time-varying characteristic and very different patterns according to the hour, time, day and week and so on, it gives rise to the uninformative results if only traditional data mining is used. Also, research on data mining for analyzing electric load patterns focused on cluster analysis and classification methods. However despite the usefulness of rules that include temporal dimension and the fact that the AMR data has temporal attribute, the above methods were limited in static pattern extraction and did not consider temporal attributes. Therefore, we propose a new classification method for predicting power load patterns. The main tasks include clustering method and temporal classification method. Cluster analysis is used to create load pattern classes and the representative load profiles for each class. Next, the classification method uses representative load profiles to build a classifier able to assign different load patterns to the existing classes. The proposed classification method is the Calendar-based temporal mining and it discovers electric load patterns in multiple time granularities. Lastly, we show that the proposed method used AMR data and discovered more interest patterns.

• Journal of the Korean Society of Safety
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• v.26 no.5
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• pp.105-109
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• 2011

Since a severe accident, which happens in low frequency, can cause serious damages, the interests in off-site consequence analysis for a nuclear power plant have been increased after Chernobyl, TMI and Fukushima accidents. Consequences, which are the effects on health and environment caused by released radioisotopes, are evaluated using MACCS II code based on the method of Level 3 PSA. To perform a consequence analysis for the reference plants, the input data of the code were generated such as meteorological data, population distribution, release fractions, and so on. Using these input data, acute and lifetime dose as an organ, CCDF for early fatalities and latent cancer fatalities, and average individual risk were analyzed by using MACCS II code in this study. These results might contribute to establishing accident management plan and quantitative health object.