• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.19 no.10
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• pp.942-946
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• 2006

We simulated insulation defects of stator winding wire on high voltage generator by 5 types. 4 types have one discharge source and other one has multi discharge source by simulation. For accurate decision, measurements used to PRPD pattern to occurred partial discharge source of various types. In this research, when PRPD pattern carried out or analyzed pattern recognition of discharge source, it used to powerful tools. In this result, PRPD Pattern defined to have single discharge source of 4 types by insulation defect. When insulation defect simulated, all the defected winding have not the same result. Errors for a little different can make mistakes from a subtle distinction. The difference between internal and void discharge have magnitude of pulse amplitude of inner discharge bigger than void discharge and have a shape of bisymmetry. But void discharge has a shape of bisymmetry against maximum value on polarity respectively. In cases of slot and surface discharge, we confirmed to show similar results those other researchers. In case of multi-discharge, as a result of we could classify not perfect match with occurred patterns in single discharge eachother. In the future, we will have to recognize and classify with results of multi-discharge.

• Proceedings of the KIEE Conference
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• 2007.07a
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• pp.1443-1444
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• 2007

지속적으로 증가되는 전기에너지 공급의 신뢰성을 높이기 위하여 전력설비 주요 사고 원인인 부분방전(PD : Partial Discharge)을 검출하고 결함원의 패턴인식 방법의 개발 필요성 날로 증가되고 있다. 본 논문은 부분방전의 패턴인식 확률을 높이기 위하여 검출된 부분방전의 주파수 분석을 이용하여 Conventional PRPD Analysis 방법의 결함 판독확률을 향상시키기 위하여 Advanced PRPD를 제안 한다. 이를 위하여, GIS(Gas Insulated Switchgear)의 주요 사고원인으로 인식되어 있는 결함들을 인위적으로 제작 후 삽입하여 부분방전을 발생시켜 자체 설계 개발된 UHF 내장형 센서를 이용하여 검출하였다. 새로이 제안하는 방법과 기존의 PRPD 방법의 인식률을 상호 비교하기 위하여, 두 가지 그룹을, 즉, 기존의 방법에 의한 것과 부분방전의 주파수 분석이 포함된 방법에 의한 데이터그룹을 구축하고 학습방법은 동일한 인공신경망 MLP (Multilayer Perceptron)를 이용하여 인식률과 학습시간을 동시에 비교하였다. 상호 비교 결과에 의하면, 후자의 방법이 인식확률 뿐만아니라 학습시간도 좋은 결과가 나타났다.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.1
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• pp.126-131
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• 2009

This study was carried out for the reliability of PD(Partial Discharge) pattern recognition. For the pattern recognition, the database for PD was established by use of self-designed insulation defects which occur and were mostly critical in GIS(Gas Insulated Switchgear). The acquired database was analyzed to distinguish patterns by means of PRPD(Phase Resolved Partial Discharge) method and stored to the form with to unite the average amplitude of PD pulse and the number of PD pulse as the input data of neural network. In order to prove the performance of genetic algorithm combined with neural network, the neural networks with trial-and-error method and the neural network with genetic algorithm were trained by same training data and compared to the results of their pattern recognition rate. As a result, the recognition success rate of defects was 93.2% and the neural network train process by use of trial-and-error method was very time consuming. The recognition success rate of defects, on the other hand, was 100% by applying the genetic algorithm at neural network and it took a relatively short time to find the best solution of parameters for optimization. Especially, it could be possible that the scrupulous parameters were obtained by genetic algorithm.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.57 no.1
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• pp.56-59
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• 2008

We have designed 4 types(void in insulation paper, protrusion electrode, floating electrode, surface discharge) of partial discharge(PD) defect to simulate typical faults found in oil filled power transformers. Ultra-high frequency(UHF) radiation due to PD was measured using a UHF measuring system and a conventional PD measuring system, simultaneously. Electromagnetic radiation spectra of these defects show UHF radiation up to about 1.5-2 GHz range. The phase resolved partial discharge(PRPD) patterns of UHF radiation from the PD defects were also measured and the pattern reveals distinct feature for each defect types. The UHF measuring could be used to detect PDs in oil filled transformers and analysis of the PRPD pattern should provide useful information on origin of PD signal.

• Journal of Electrical Engineering and Technology
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• v.13 no.2
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• pp.902-910
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• 2018

In recent times, development of nanofluid insulation for power transformers is a hot research topic. Many researchers reported the enhancement in dielectric characteristics of nano modified mineral oils. Considering the drawbacks of petroleum based mineral oil, it is necessary to understand the dielectric characteristics of nanofluids developed with natural ester based oils. Palm oil has better insulation characteristics comparable to mineral oil. However very few research reports is available in the area of nanofluids based on palm oil. Partial discharge (PD) is one of the major sources of insulation performance degradation of transformer oil. It is essential to understand the partial discharge(PD) characteristics by collecting huge data base of PD performance of nano modified palm oil which will increase its confidence level for power transformer application. Knowing these facts, in the present work, certain laboratory experiments have been performed on PD characteristics of nano $SiO_2$ modified palm oil at different electrode configurations. Influence of concentration of nano filler material on the PD characteristics is also studied. Partial discharge inception voltage, Phase resolved partial discharge (PRPD) pattern, PD signal time-frequency domain characteristics, PD signal equivalent timelength-bandwidth mapping, Weibull distribution statistical parameters of PRPD pattern, skewness, repetition rate and phase angle variations are evaluated at different test conditions. From the results of the experiments conducted, we came to understand that PD performance of palm oil is considerably enhanced with the addition of $nano-SiO_2$ filler at 0.01%wt and 0.05%wt concentration. Significant reduction in PD inception voltage, repetition rate, Weibull shape parameter and PD magnitude are noticed with addition of $SiO_2$ nanofillers in palm oil. These results will be useful for recommending nano modified palm oil for power transformer applications.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.27 no.12
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• pp.877-882
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• 2014

It has been confirmed that the inner defect of transformer and the perfect diagnosis for aging are closely related to safe electric power transmission system and that the detection of accident and diagnosis technique turn out to be very important issues. Since electric power machinery consists of various kinds of components, however, it is very difficult to make a diagnosis for aging by one parameter. Thus, diagnosis for aging is feasible only through the combination of various parameters. Recently, various expert systems have been developed and applied to diagnosis for aging, but they are not yet reliable enough to apply to the real system. In this paper, XLPE which is ultra high voltage cable insulator material were chosen to investigate the influence of void on insulator material using partial discharge. Obtained data have been processed by PRPD (phased resolved partial discharge) distribution function and K-means. And statistical and cluster distribution of partial discharge have been analysed and investigated.

• The Transactions of the Korean Institute of Electrical Engineers C
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• v.55 no.6
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• pp.308-312
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• 2006

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 Partial Discharge(PD) phenomena is suggested to be considered as a deterministic dynamical process. In this work, for the diagnosis of Gas Insulated Transformer(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 Gas Insulated Transformer(GITr) mock-up and experimental investigations have been carried out in order to analyze the related Partial Discharge(PD) patterns by means of both Phase Resolved Partial Discharge Analysis(PRPDA) and Chaotic Analysis(CAPD) respectively and then their comparisons are made systematically.

• 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.

• 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.

• 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.