• 한국전기전자재료학회:학술대회논문집
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• 한국전기전자재료학회 2006년도 하계학술대회 논문집 Vol.7
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• pp.83-84
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• 2006

In this paper, we examine discharge characteristics of cross-linked polyethylene (since then; XLPE) according to thickness. Voltage was applied to power frequency by step method, and calibration of discharge was set to 50[pC] (slope=8.333). After the voltage was applied, for 10 [sec] (600 [cycle]), occurring discharge and number were detected. Determine of input pattern is difficult because discharge pattern is irregular. Therefore we investigated pattern using the K-means Analysis and Weibull function. Also we investigated variation of centroid and cluster.

• 한국화재조사학회학술대회
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• 한국화재조사학회 2011년도 제21회 춘계학술대회
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• pp.137-160
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• 2011

본 사고분석을 통해 154 kV 절연부싱에서의 폭발사고에 대한 원인을 규명하였다. 결과적으로, 절연부싱의 사양은 국제표준에 적합하였다. 사고당일 기록된 자료에 의하면 R상과 S상에서 거의 동시에 지락사고가 발생하였으며, 지락지속시간은 약 75 ms로써 사고의 영향을 준 시간은 약 67 ms인 것으로 나타났다. R상은 아크에 의한 탄화 흔적, S상은 아크에 의한 탄화흔적과 외부열에 의한 탄화흔적, T상은 외부열에 의한 탄화흔적, 용융흔적은 R상과 S상의 케이블접속부와 플랜지에서 각각 발생하였다. S상의 절연부싱을 이용하여 탄화패턴 중 아크에 의한 것과 일반 열에 의한 것을 분류하여 연면방전이 발생한 것을 입증하였다. 사고추정 시나리오는 현장조사과정에서 나타난 현상과 목격자 진술, 사고원인 분석자료 등을 토대로 하여 작성되었다. 따라서 사고추정을 통해 분석된 자료는 아크생성단계, 열폭주 단계, 폭발단계, 화재단계로 구성하였다. 사고원인 가능성은 사고의 원인, 형태, 영향을 통해 나타난 연결고리를 검토하여 가능성이 낮은 부분을 배제하는 방식으로 진행되었다. 절연부싱의 사고원인은 표면의 오염물질 부착 가능성이 가장 높았다. 이를 근거로 하여 제조, 시공, 관리적 측면에서의 방지대책을 고려하는 것이 바람직할 것으로 판단된다.

• 대한전자공학회논문지SD
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• 제45권11호
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• pp.8-12
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• 2008

AC PDP에 사용되는 ITO 전극의 공정시간을 단축시키고 생산성을 향상시키기 위해서 Nd:$YVO_4$ laser를 사용하여 ITO 전극 패턴을 하였다. ITO etchant를 사용하여 ITO 전극패턴을 형성한 샘플과 비교해서 laser를 사용하여 제작한 샘플은 ITO 라인 끝 부분에 shoulder와 물결무늬가 형성되었다. shoulder와 물결무늬의 제거를 위해서 laser의 펄스반복율과 스캔 속도에 변화를 주었다. 또한 shoulder와 물결무늬를 갖는 ITO 전극이 PDP에 주는 영향을 알아보기 위해서 방전특성분석을 하였다. 실험결과 40 kHz와 500 mm/s를 기본 조건으로 결정하였다. 본 실험을 통하여 레이저를 이용한 PDP용 ITO 전극막의 직접 패터닝 가능성을 확인할 수 있었다.

• 한국전기전자재료학회논문지
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• 제19권10호
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• pp.947-952
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• 2006

It has been confirmed that the inner defect of insulator 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. But perfect diagnosis is difficult because discharge pattern is irregular. Thus, we investigated discharge pattern using the new distribution statistical models with cross-inked polyethylene(XLPE) specimens. Voltage was applied to power frequency by step method, and calibration of discharge was set to 50 pC. After the voltage was applied, it measured the discharge occurring during 10s. We investigated discharge pattern using the K-means analysis and Weibull function. We also investigated variation of centroid and shape parameter due to variation of voltage. As a result of analyzing K-means, it was confirmed that cluster including many object numbers was formed by the presence of void. And result of Weibull distribution, it was confirmed that shape parameter of discharge varied from 1.28 to 1.62 in no void specimens, and that shape parameter of discharge number varied from 1.28 to 1.62. In the void, shape parameter of discharge varied from 5.66 to 6.43, and shape parameter of discharge number varied from 5.05 to 5.08.

• 전기학회논문지
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• 제56권6호
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• pp.1092-1098
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• 2007

Since one decade, the detection of HFPD (High frequency Partial Discharge) has been proposed as one of the effective method for the diagnosis of the power component under service in power grids. As a tool for HFPD detection, Metal Foil sensor based on the embedded technology has been commercialized for mainly power cable due to its advantages. Recently, for the on-site noise discrimination, several PA (Pulse analysis) methods have been reported and the related software, such as Neural Network and Fuzzy, have been proposed to separate the PD (Partial Discharge) signals from the noises since their wave shapes are completely different from each other. On the other hand, the relevant fundamental investigation has not yet clearly made while it is reported that the effectiveness of the current methods based on PA is dependant on the types of sensors. Moreover, regarding the identification of the vital defects introducible into the Power Cable, the direct identification of the nature of defects from the PD signals through Metal Foil coupler has not yet been realized. As a trial for solving above shortcomings, different types of software have been proposed and employed without any convincing probability of identification. In this regards, our novel algorithm 'PA Map' based on the pulse analysis is suggested to identify directly the defects inside the power cable from the HFPD signals which is output of the HFCT and metal foil sensors. This method enables to discriminate the noise and then to make the data analysis related to the PD signals. For the purpose, the HFPD detection and PA (Pulse Analysis) system have been developed and then the effect of noise discrimination has been investigated by use of the artificial defects using real scale mockup. Throughout these works, our system is proved to be capable of separating the small void discharges among the very large noises such as big air corona and ground floating discharges at the on-site as well as of identifying the concerned defects.

• 전기학회논문지
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• 제66권9호
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• pp.1392-1401
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• 2017

In this study, the design methodology of pattern classification is introduced for avoiding faults through partial discharge occurring in the power facilities and local sites. In order to classify some partial discharge types according to the characteristics of each feature, the model is constructed by using the Radial Basis Function Neural Networks(RBFNNs) and Particle Swarm Optimization(PSO). In the input layer of the RBFNNs, the feature vector is searched and the dimension is reduced through Principal Component Analysis(PCA) and PSO. In the hidden layer, the fuzzy coefficients of the fuzzy clustering method(FCM) are tuned using PSO. Raw datasets for partial discharge are obtained through the Motor Insulation Monitoring System(MIMS) instrument using an Epoxy Mica Coupling(EMC) sensor. The preprocessed datasets for partial discharge are acquired through the Phase Resolved Partial Discharge Analysis(PRPDA) preprocessing algorithm to obtain partial discharge types such as void, corona, surface, and slot discharges. Also, when the amplitude size is considered as two types of both the maximum value and the average value in the process for extracting the preprocessed datasets, two different kinds of feature datasets are produced. In this study, the classification ratio between the proposed RBFNNs model and other classifiers is shown by using the two different kinds of feature datasets, and also we demonstrate the proposed model shows superiority from the viewpoint of classification performance.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2006년도 제37회 하계학술대회 논문집 전기설비
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• pp.55-56
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• 2006

In prevention and diagnostic system of GIS, pattern classification is focused on the detection of unnatural patterns in PD(Partial discharge) image data. Fractals have been used extensively to provide a description and to model mathematically many of the naturally occurring complex shapes, such as coastlines, mountain ranges, clouds, etc., and have also received increased attention in the field of image processing, for purposes of segmentation and recognition of regions and objects present in natural scenes. Among the numerous fractal features that could be defined and used for image data, fractal dimension and lacunarity have been found to be useful for recognition purposes Partial discharge(PD) occuring in GIS system is a very complex phenomenon, and more so are the shapes of the various 2-d patterns obtained during routine tests and measurements. It has been fairly well established that these pattern shapes and underlying defects causing PD have a 1:1 correspondence, and therefore methods to describe and qunatify these pattern shapes must be explored, before recognition systems based on them could be developed. The computed fractal features(fractal dimension and lacunarity) for standard library of PD data were analyzed and found to possess fairly reasonable pattern discriminating abilities. This new approach appears promising, and further research is essential before any long-term predictions can be made.

• 전기학회논문지
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• 제67권1호
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• pp.114-123
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• 2018

This paper concerns a design and learning method of softmax function neural networks based on K-means clustering. The partial discharge data Information is preliminarily processed through simulation using an Epoxy Mica Coupling sensor and an internal Phase Resolved Partial Discharge Analysis algorithm. The obtained information is processed according to the characteristics of the pattern using a Motor Insulation Monitoring System program. At this time, the processed data are total 4 types that void discharge, corona discharge, surface discharge and slot discharge. The partial discharge data with high dimensional input variables are secondarily processed by principal component analysis method and reduced with keeping the characteristics of pattern as low dimensional input variables. And therefore, the pattern classifier processing speed exhibits improved effects. In addition, in the process of extracting the partial discharge data through the MIMS program, the magnitude of amplitude is divided into the maximum value and the average value, and two pattern characteristics are set and compared and analyzed. In the first half of the proposed partial discharge pattern classifier, the input and hidden layers are classified by using the K-means clustering method and the output of the hidden layer is obtained. In the latter part, the cross entropy error function is used for parameter learning between the hidden layer and the output layer. The final output layer is output as a normalized probability value between 0 and 1 using the softmax function. The advantage of using the softmax function is that it allows access and application of multiple class problems and stochastic interpretation. First of all, there is an advantage that one output value affects the remaining output value and its accompanying learning is accelerated. Also, to solve the overfitting problem, L2-normalization is applied. To prove the superiority of the proposed pattern classifier, we compare and analyze the classification rate with conventional radial basis function neural networks.