• Proceedings of the KIEE Conference
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• 2004.05b
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• pp.109-112
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• 2004

In this study, the applicability of SOM(Self Organizing Map) algorithm to partial discharge pattern recognition have been investigated. For the purpose, using acquired data from the artificial defects in GIS, SOM 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. As a result, basically BP algorithm was found out to be better than SOM algorithm. Therefore, it is needed to apply SOM algorithm in combination with BP algorithm in order to improve on-site applicability using the advantages of SOM. Also, for the pattern recognition by use of PRPDA(Phase Resolved Partial Discharge Analysis) it is required the normalization of the PRPDA graph. However, in case of the normalization both BP and SOM algorithm have shown worse results, so that it is required further study to solve the problem.

• Proceedings of the KIEE Conference
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• 2007.07a
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• pp.1445-1446
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• 2007

In this paper, we have studied for analysis of the partial discharge(PD) signal in power transmission line. The PD signal has estimated as detected signal accumulation of a PRPDA method by using Labview, and analyzed with neural-fuzzy algorithm. With practical PD logic implementation of theoretical detected system and hardware implementation, the device for Hipotronics Company's 22.9kV or 154kV setup have generated and then have applied with 18kV,20kV with 1:1 time probe. It's also used the LDPE 0.27mmt (scratch error 0.05mmt) to sample for making PD. Our new class of PD detected algorithm have also compared with previous PRPDA or Neural Fuzzy algorithm, which has diagnose more conveniently by adding numerical values.

• The Transactions of the Korean Institute of Electrical Engineers C
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• v.54 no.5
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• pp.198-205
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• 2005

Many precedent investigations hate been made for the reliable assessment of the insulation state of large power apparatus for which partial discharge detection is one of tile plausible way. In this work, experimental investigations have been carried out to make the comparison on the PD(partial discharge) pattern analysis related to the five different types of artificial defects such as SFMP (Single Free Moving Particle), MFMP (Multi Free Moving Particle), Void, CFP (Conductor-Fixed Protrusion), EP (Enclosure Protrusion). For each PD pattern, PD detection has been done by tee different types of PD sensors such as HFCT(High Frequency Current Transformer), AE(Acoustic Emission) and UHF(Ultra High Frequency). And, in addition, frequency spectrum by the UHF sensor has been also made for each defect respectively. As a result, it is observed that the possibility of obtaining PD pattern based on PRPD(Phase Resolved Partial Discharge) in connection with the defects tinder investigation is dependant on the type of the sensor while the spectrum analysis is always successful to be achieved for every defect. Therefore, it could be suggested that the nature of PD source can be identified more distinctively when the conventional PRPDA is combined with spectrum analysis.

• 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.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.12
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• pp.2313-2321
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• 2008

In order to develop reliable on-site partial discharge(PD) pattern recognition algorithm, the fuzzy neural network based on fuzzy set(FNN) and the polynomial network pattern classifier based on fuzzy Inference(PNC) were investigated and designed. Using PD data measured from laboratory defect models, these algorithms were learned and tested. Considering on-site situation where it is not easy to obtain voltage phases in PRPDA(Phase Resolved Partial Discharge Analysis), the measured PD data were artificially changed with shifted voltage phases for the test of the proposed algorithms. As input vectors of the algorithms, PRPD data themselves were adopted instead of using statistical parameters such as skewness and kurtotis, to improve uncertainty of statistical parameters, even though the number of input vectors were considerably increased. Also, results of the proposed neuro-fuzzy algorithms were compared with that of conventional BP-NN(Back Propagation Neural Networks) algorithm using the same data. The FNN and PNC algorithms proposed in this study were appeared to have better performance than BP-NN algorithm.

• 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.

• Proceedings of the KIEE Conference
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• 2005.07c
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• pp.2060-2062
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• 2005

Partial Discharge (PD) phenomena occurred by different nature of insulating defects has been regarded as a random process by which Phase Resolved Partial Discharge Analysis (PRPDA) has been proposed and then commercially accepted for the diagnosis of the power apparatus since more than three decades. Moreover, for the same purpose, a novel approach based on the Chaotic Analysis(CAPD) has been proposed since 2000, in which PD phenomena is suggested to be considered as a deterministic dynamical process. In this work for the diagnosis of GITr, four different types of specimen were fabricated as a model of the possible defects that might possibly cause its sudden failures such as turn to turn insulation, inter coil insulation, free moving particle and protrusion. For this purpose, these defects are introduced into the GITr mock-up and experimental investigations have been carried out in order to analyze the related PD patterns by means of both PRPDA and CAPD respectively and then their comparisons are made systematically.

• Proceedings of the KIEE Conference
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• 2011.07a
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• pp.1558-1559
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• 2011

지난 10년여간의 국내외 연구 결과 배전 케이블에 대한 부분방전 측정기술의 정확성은 상당히 향상되었고 그 결과물로서 수많은 측정 장비와 기법이 개발되어 오늘날 널리 보급되고 있다. 하지만 여전히 측정된 Data에 대한 해석은 일부 전문가들에게 조차 복잡하고 어려운 일로 남아 있다. 자칫 그릇된 해석은 향후 큰 사고로 이어질 수 있으므로 선로 운영자의 입장에서는 측정결과의 신뢰성에 민감할 수밖에 없다. 이러한 신뢰성을 높이 고자 대부분의 배전 케이블 구성이 3상으로 구성되어 있고 임의의 상에서 부분방전이 발생했을시 PRPDA(Phase Resolved Partial Discharge Analysis)기법으로는 발생상 확인에 한계가 있다. 이러한 단점을 보안 하고자 본 논문에서는 3PARD 분석 기법을 활용한 발생상 확인 및 검증을 하였다.

• Proceedings of the KIEE Conference
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• 2009.07a
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• pp.1457_1458
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• 2009

지난 10년여간의 국내외 연구 결과 배전 케이블에 대한 부분방전 측정기술의 정확성은 상당히 향상되었고 그 결과물로서 수많은 측정 장비와 기법이 개발되어 오늘날 널리 보급되고 있다. 하지만 여전히 측정된 Data에 대한 해석은 일부 전문가들에게 조차 복잡하고 어려운 일로 남아 있다, 자칫 그릇된 해석은 향후 큰 사고로 이어질 수 있으므로 선로 운영자의 입장에서는 측정결과의 신뢰성에 민감할 수밖에 없다. 이러한 신뢰성을 높이 고자 기존에 많이 사용되고 있는 PRPDA기법의 단점을 보안 할 수 있는 TF-MAP을 활용한 배전 케이블 부분방전 측정 시스템을 개발 하였다.

• 기계와재료
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• v.21 no.3
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• pp.62-73
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• 2009

전력기기에서 발생하는 부분방전을 정확히 측정하고 이를 올바르게 해석하는 작업은 신뢰성 있는 진단법을 개발하고 이를 현장에 적용하는데 있어 대단히 중요하다. 측정된 고주파 데이터를 패턴 분석이 가능한 형태로 가공하는 전처리 과정을 수행하고, 가공된 데이터를 패턴인식을 통하여 기존의 각 노이즈 및 부분방전 패턴과 비교하여 실제 측정된 데이터가 어떤 부분방전 패턴인지 판단한다. 패턴 인식 처리 방법으로는 컴퓨터 분야 신경회로망의 BP 알고리즘과 SOM 알고리즘이 널리 사용되고 있으며 본 연구에서는 TF-MAP, PRPDA, EBP 알고리즘을 이용하여 부분방전 패턴인식 기술 개발에 관한 연구를 수행하였다.