• Title/Summary/Keyword: Defective Insulator

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Defective Porcelain Insulator Inspection Based on Harmonic Retrieval (고조파 추출을 이용한 불량애자 검출장치 개발연구)

  • Lu, Hao;Jin, Hong-Zhe;Han, Sun-Sin;Lee, Jang-Myung
    • Proceedings of the IEEK Conference
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    • 2007.07a
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    • pp.291-292
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    • 2007
  • Porcelain insulators are widely used in overhead high-voltage power transmission lines while providing adequate insulation to withstand switching and lightning over voltages. For the safety consideration, we proposed a novel insulator inspection method using harmonic, which is retrieved from the low frequency signal. The working principle of this new method is based on the relationship between the low frequency harmonic and the defective characteristic of the insulators. So, in this paper, the harmonic retrieval in the complex noise is solved with the HOC (High Order Cumulants) is extended. In the experiment, as one of our dedicated contribution, we illustrate low frequency harmonic and the defective characteristics of the porcelain insulators.

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Automatic Visual Inspection System -Detection of Insulator′s Minute Crack- (자동 시각 검사 시스템 -현수애자의 미세균열 검출-)

  • 이상용;김용철
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • 2004.04a
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    • pp.576-579
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    • 2004
  • Eventhough the productivity has been improved remarkably by introducing automatic facilities, the 100% inspection is necessary because the possibility to produce large amount of defective goods is also increased. Since it is extremely unreasonable that workers inspect very large amount of products as 100% inspection, there has been many researches for the automatic inspection system. In this thesis, we develop an automatic detection system of suspension insulator's minutes cracks System The automatic detection system of suspension insulator's minute cracks: To detect the minute cracks of suspension insulators, images of the insulator are acquired with a progressive scan camera, rotating a suspension insulator on a turning table. And after the shadow and noises are eliminated by preprocessing techniques, we detect minute cracks using the features of them.

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Analysis on the Degradation of Insulators using Corona Camera (코로나 카메라를 이용한 불량애자 검출사례 분석)

  • Kim, H.S.;Kim, P.H.;You, Y.K.;Koo, K.W.;Kim, H.J.;Kim, D.S.
    • Proceedings of the KIEE Conference
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    • 2003.07e
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    • pp.132-134
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    • 2003
  • Insulator that is used in electric power equipment superannuation and being damaged disorder can. Such as defected insulator has repair or change must because defective influence in electrical equipment. There are many method developed for detecting the defected insulators, analyzed various test of devide voltage, electric field, ultrasonic and discharge pulse. But methods of detecting ultrasonic of discharge, electric field or leakage current could not application in the field, In this paper, we compare the advantage and the limitation of the method to detect defected insulator, and wish proposed using corona camera.

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A Study on the time domanin feature extraction of EM radiation wave due to high AC voltage discharge (교류 고전압 방전에 의한 방사 전자파의 시간 영역 특징 추출에 관한 연구)

  • Kang, Dae-Soo;Lim, Seung-Gag
    • The Journal of Korea Institute of Information, Electronics, and Communication Technology
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    • v.1 no.1
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    • pp.41-45
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    • 2008
  • When 60Hz AC voltage supplied to a falty insulator, Radiated EM wave has characterized time distribution with the arrived periodicity. For classifying distribution feature, receiving frequency and bandwidth of radiated wave be experimented and determined. Since the spectrum of the radiated wave has broad bandwidth and time-variable statistics, the receiving quality is determined receiving bandwidth that has above 900kHz.

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Effective Analsis of GAN based Fake Date for the Deep Learning Model (딥러닝 훈련을 위한 GAN 기반 거짓 영상 분석효과에 대한 연구)

  • Seungmin, Jang;Seungwoo, Son;Bongsuck, Kim
    • KEPCO Journal on Electric Power and Energy
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    • v.8 no.2
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    • pp.137-141
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    • 2022
  • To inspect the power facility faults using artificial intelligence, it need that improve the accuracy of the diagnostic model are required. Data augmentation skill using generative adversarial network (GAN) is one of the best ways to improve deep learning performance. GAN model can create realistic-looking fake images using two competitive learning networks such as discriminator and generator. In this study, we intend to verify the effectiveness of virtual data generation technology by including the fake image of power facility generated through GAN in the deep learning training set. The GAN-based fake image was created for damage of LP insulator, and ResNet based normal and defect classification model was developed to verify the effect. Through this, we analyzed the model accuracy according to the ratio of normal and defective training data.