• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.8
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• pp.1436-1441
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• 2016

Currently, partial discharge diagnosis in the field of prevention applied technology and diagnostic equipment is a possible strong limit to remove the noise generated by external or internal I still have one unreliable diagnosis. This technology is the noise removal from signal the time lag analysis algorithms technique is applied by a fundamental. Increasing the reliability in terms of technology spectrum frequence of analysis method for by applying the acquisition through the position of the frequency content and sources of traffic lights partial discharge of the acquisition of signal analysis to judge whether a way diagnosis the environment of the scene, and conditions. Partial discharge signal and make the discharge while building blocks were found through the Analysis. Spectrum frequence of Analysis and wide discharge part, to be more precise, in line with the various functions, including the analysis technique band. Diagnosis and comes up with advanced technology that can detect the presence of a position. This method is portable single device developed for maintenance and mobility and ease and convenience of getting caught by discharge of the pattern analysis and position detection method suitable for a new diagnosis will suggest.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2001.05c
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• pp.33-37
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• 2001

Recently, the HFPD measurement testing is widely used in partial discharge measurement of HV machines because HFPD measurement testing receives less influence of external noise and has a merit of good sensitivity. Also HFPD testing is able to offer the judgement standard of degradation level of HV machine and can detect discharge signals in live-line. Therefore it is very useful method compare to previous conventional PD testing method and effective diagnosis method in power transformer that requires live-line diagnosis. But partial discharges have very complex characteristics of discharge pattern so it is required continuous research to development of precise analysis method. In recent, the study of partial discharge is carrying out discover of initial defect of power equipment through condition diagnosis and system development of degradation diagnosis using HFPD(High Frequency Partial Discharge) detection. In this study, simulated transformer is manufactured and HFPD occurred from transformer is measured with broad band antenna in real time, the degradation grade of transformer is analyzed through produced patterns in simulated transformer according to applied voltages.

• Proceedings of the KIEE Conference
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• 2004.07c
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• pp.1930-1932
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• 2004

SOM(Self Organizing Map) algorithm which has some advantages such as data accumulation ability and the degradation trend trace ability was compared with conventionally used BP(Back Propagation) algorithm. For the purpose, partial discharge data were acquired and analysed from the artificial defects in GIS. As a result, basically the pattern recognition rate of BP algorithm was found out to be better than that of SOM algorithm. However, SOM algorithm showed a great on-site-applicability such as ability of suggesting new-pattern-possibility. Therefore, through increasing pattern recognition rate it is possible to apply SOM algorithm to partial discharge analysis. Also, for the image processing method it is required the normalization of the PRPDA graph. However, due to the normalization both BP and SOM algorithm have shown worse results, so that it is required further study to solve the problem.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.17 no.1
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• pp.31-36
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• 2003

This paper describes the quantitative analysis and the pattern recognition of partial discharge signals generated by particles in GIS. Four states of particles were simulated in this paper. Partial discharge signals from each state was measured and the Ф-Q-N distribution of partial discharge signals was displayed and then the Ф-Q, the Ф-Qm, the Ф-N and the Q-N distribution were displayed. Each distribution can be quantitatively represented by statistical parameters and the parameters were used for input data of pattern recognition. As the results, it was found that the forms of each distribution were different according to the particle states. Recognition rate using neural network was about 92〔%〕 and the more input data number, the more accurate results.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.8
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• pp.1615-1623
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• 2009

In order to develop reliable on-site partial discharge (PD) pattern recognition algorithm, we introduce Type-2 Fuzzy Neural Networks (T2FNNs) optimized by means of Particle Swarm Optimization(PSO). T2FNNs exploit Type-2 fuzzy sets which have a characteristic of robustness in the diverse area of intelligence systems. Considering the on-site situation where it is not easy to obtain voltage phases to be used for PRPDA (Phase Resolved Partial Discharge Analysis), the PD data sets measured in the laboratory were artificially changed into data sets with shifted voltage phases and added noise in order to test the proposed algorithm. Also, the results obtained by the proposed algorithm were compared with that of conventional Neural Networks(NNs) as well as the existing Radial Basis Function Neural Networks (RBFNNs). The T2FNNs proposed in this study were appeared to have better performance when compared to conventional NNs and RBFNNs.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.51 no.6
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• pp.276-283
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• 2002

This Paper Presents the method for detecting a partial discharge(PD) using an electromagnetic wave measured by an antenna. The various electromagnetic waves are measured in the laboratory and wavelet transform, which is provides a direct quantitative measure of spectral content in the time-frequency domain, are applied for identifying the property of electromagnetic waves. Also, the statistical method and self-organizing feature map(SOFM) are applied for the pattern recognition of electromagnetic waves. The proposed method is shown to be useful for detecting electromagnetic waves emitted for PD in test data.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2004.07b
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• pp.1175-1178
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• 2004

On-line partial discharge(PD) monitoring system has been developed to monitor in operating large motor stator insulations. This system makes use of remote diagnosis techniques for the evaluation of PD activity in the control center of thermal power plant. This system can be remotely accessed via a modem to build database, analyze status and interpret the pattern of PD activity. A personnel computer is generally connected to ten motors to continuous measurement of the PD activity. The test data can be easily interpreted by a maintenance staff. For assessing the condition of stator winding in motors, this system ensures a reliable measurement and accurate estimation. Capacitive couplers used for on-line PD measurement have been 80pF. The maximum PD magnitude(Qmax), PD pattern and normalized quantity number(NQN) were performed by this system.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2004.11a
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• pp.327-330
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• 2004

Because of its effectiveness for the PD(partial discharge) pattern recognition, PSA(Pulse Sequence Analysis) has been considered as a new analytic method instead of conventional PRPDA(Phase Resolved Partial Discharge Analysis). However, PSA has a big problem that can misanalyze patterns in case of data missing resulting from poor sensitivity because it analyses the correlation between sequential pulses, which leads to hesitate to apply it to on-site. Therefore, in this paper, the problems of PSA such as data missing and noise adding cases were investigated. For the purpose, PD data obtained from various defects including noise adding data were used and analysed, The result showed that both cases can cause fatal errors in recognizing PD patterns. In case of the data missing, the error depends on the kinds of defect and the degree of degradation. Also, it could be noticed that the error due to adding noises was larger than that due to some data missing.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.18 no.5
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• pp.484-489
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• 2005

Because of its effectiveness for the PD(Partial Discharge) pattern recognition, PSA(Pulse Sequence Analysis) has been considered as a new analytic method instead of conventional PRPDA(Phase Resolved Partial Discharge Analysis). However, it is generally thought that PSA has some possibility to misjudge patterns in case of data-missing resulting from poor sensitivity because it analyses the correlation between sequential pulses, which leads to hesitate to apply it to on-site. Therefore, in this paper, the problems of PSA such as data-missing and noise-adding cases were investigated. for the purpose, PD data obtained from various defects including noise-adding data were used and analyzed. As a result, it was shown that both cases could cause fatal errors in recognizing PD patterns. In case of the data missing, the error was dependant on the kinds of defect and the degree of degradation Also, it could be noticed that the error due to adding noises was larger than that due to some data missing.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.26 no.2
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• pp.151-157
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• 2013

In this paper, properties of pattern using LBG (Linde-Buzo-Gray) Algorithm was explored including the exactness of K-means algorithm and process time of EM (Expectation Maximization) algorithm in order to develop analysis algorithm of partial discharge pattern in a cable using acoustic data analysis system. Partial discharge was measured by generating inner fault due to lamination of XLPE which is used for cable insulation material. Discharge pattern was analysed by changing the number of swarm article to 2, 4, and 6 in order to interpret swarm structure and properties.