• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2002.07b
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• pp.882-885
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• 2002

Assessing the condition of air insulation by measuring electromagnetic wave generated in it by partial discharges gives warning of developing faults and breakdown which may give some bad effects to other thing concerned with it. Electromagnetic wave are taken from UHF antenna with 20k ~ 2GHz frequency range. The signal waveform and FFT(Fast Fourier Transform) results from UHF antenna for PD(partial discharge) generated from two different electrode(needle-plane, needle-needle) configurations are shown and it is processed by using PRPD($\Phi-q-n$ distribution) method. Cumulative pulse sequence during 100 period is compared with PRPD method. This also will show the possibility of making apparent distinction between two PD sources.

• KIEE International Transactions on Electrophysics and Applications
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• v.5C no.2
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• pp.63-66
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• 2005

This paper describes analysis and pattern recognition techniques for Partial Discharge(PD) signals in Gas Insulated Switchgears (GIS). Detection of PD signals is one of the most important factors in the predictive maintenance of GIS. One of the methods of detection is electro magnetic wave detection within the Ultra High Frequency (UHF) band (300MHz $\~$ 3GHz). In this paper, PD activity simulation is generated using three types of artificial defects, which were detected by a UHF PD sensor installed in the GIS. The detected PD signals were performed on three-dimensional phi-q-n analysis. Finally, parameters were calculated and an Artificial Neural Network (ANN) was applied for PD pattern recognition. As a result, it was possible to discriminate and classify the defects.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.20 no.10
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• pp.901-906
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• 2007

In this study, we measured the partial discharge phenomenon of inside the closed switchgear, using ultra wide band antenna. The characteristics of $\Phi-q-n$ in the normal state are stable, and confirmed at less than 0.01, but in proceeding states, about 2 times larger. And in the abnormal state, it grew hundreds of times larger compared with normal state. According to K-means analysis, if slant of discharge characteristics is a straight line close to "0" and standard deviation is small, it is in a normal state. However if we can find a peak from K-means clusters and standard deviation to be large, it is in an abnormal state.

• Proceedings of the KIEE Conference
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• 1996.07c
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• pp.1781-1783
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• 1996

Recently, the necessity of establishing the way to diagnose the aging of insulation materials and to predict of insulation breakdown become important. The purpose of our work are to investigate the treeing phenomena with a artificial needle shape void by the charge of partial discharge and acoustic emission pulse in each phase angle area at the same time. We have analyzed the ${\Phi}-Q-n$ pattern and the insulation diagnosis of the samples using statistic operators such as charge magnitude and A.E pulse factor, skweness, kurtosis, G,C. Therefore, the relation between the charge of partial discharge and A.E pulse will be helpful and efficient to predict the breakdown just before the breakdown occure.

• 한국컴퓨터산업교육학회:학술대회논문집
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• 2003.11a
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• pp.55-58
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• 2003

Stability of inside power equipments are required for electron equipment in order to operate those safely. Also, the power circuits for these have been developing, but research of dielectric strength of power supply is insufficient. In this paper, for test insulation condition variation by void it is tested insulation characteristics variation, using $\phi-q-n$ distribution variation in partial discharge test.

• Proceedings of the KIEE Conference
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• 1999.11d
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• pp.1084-1086
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• 1999

The breakdown was caused by particle in GIS and various methode was suggested to find particle in GIS. In this paper, four states of particle was imitated which are particle on electrode, particle on enclosure, particle on spacer and crossing particle. The discharge voltage of each states was classified by four voltages which are initial, development, later and close discharge voltage. $\Phi$-Q-N pattern was analyzed according to the states and to the discharge voltages. In the result, the electrical discharge pattern according to the particle states may be distinguished.

• Proceedings of the KSR Conference
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• 2009.05a
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• pp.1977-1983
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• 2009

This paper described partial discharge(PD) patterns depending on types of insulation defects in CNCO-W cable(Concentric Neutral Closs-linked Polyethylene Insulated Polyolefin-Water Proof Sheathed Power Cable). The PD measurement system consists of a coupling network, a detection impedance, and a low noise amplifier. A 16 bit, 250 MS/s data acquisition system was used to analyze PD patterns. To simulate insulation defects in a power cable, a needle with the curvature radius of $10{\mu}m$ was inserted into the insulation part. We measured phase ($\Phi$), magnitude (q), and counts (n) of PD pulse for the defects, and classified PD patterns using the PRPD (phase Resolved Partial Discharge) method. From the analysis of acquired PD signals, we could find that a unique PD pattern is formed according to the types of defect.

• Proceedings of the KIEE Conference
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• 2000.07c
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• pp.1951-1953
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• 2000

Partial Discharge is non-disruptive discharge, which can be discriminated to 3 kinds as firstly, internal discharge which is caused by internal voids inside of insulator, secondly, surface discharge which occurs from the surface of insulator and finally, corona discharge which is from the surface of an electrode. In this thesis, three kinds of defects are artificially made to the inside and the surface of the epoxy resine busing with internal voltage detection sensor, which is practically equipped into SF6 gas insulated switchgear for 22.9kV distribution line, so that discrimination of three kinds of PD signals by means of phi-q-n pattern analysis method was performed.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.4
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• pp.635-640
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• 2008

New gas mixtures are now finding applications such as interrupting media for high-voltage circuit breakers. These mixtures consist of a high content of carbon tetrafluoride($CF_4$) added to sulfur hexafluoride($SF_6$). Nowdays $SF_6$ has been established for the use in gas insulated substations due to its high insulation withstand level and good arc quenching capability. At this paper Breakdown characteristics were investigated for $SF_6/CF_4$ mixtures when AC voltage and standard lightning impulse voltage(LI) was applied in a needle-plane electrodes. And partial discharge(PD) experiments were carried out in the test chamber which was made in needle-plane electrode. And ${\Phi}$-Q-N distribution of partial discharge signals was analyzed. The total pressure of the $SF_6/CF_4$ mixtures was varied within the range of 0.1-0.5 Mpa in the test chamber. The breakdown voltage in needle-plane electrode displayed N shape characteristics for increasing the content of $SF_6$ at positive impulse voltage and the PD inception voltage was increased slightly when pressure of $SF_6/CF_4$ Mixtures was increased. Maximum PD inception voltage is showed in 80% SF6/20%$CF_4$.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2004.11a
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• pp.653-657
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• 2004

In this study, PD(partial discharge) signals which occurrs at stator coil of traction Motor are acquired. these data are used for classifying the PD sources. W(Neural Network) has recently applied to classify the PB pattern. The PD data are used for the learning process to classify PD sources. The PD data come from normal specimen and defective specimens such as internal void discharges, slot discharges and surface discharges. PD distribution parameters are calculated from a set of the data, which is used to realize diagnostic algorithm. NN which applies distribution parameters is useful to classify the PD patterns of defective sources generating in stator coil of traction motor.