• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.28 no.4
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• pp.16-20
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• 2014

To prevent any accident in electric power utilities, many researches for inspection and diagnosis deteriorations occurring by corona discharges have been continuously studying. Inspection and diagnosis of electric power utility is very important to prevent an accident. This paper studies a measurement of ultra-violet(UV) ray of corona discharges on polymer insulators using an UV sensor array with an optic lens. The detection of an UV signal begins at 60kV, which is about 37.5% of the breakdown voltage of the polymer insulator and the stronger the high voltage increased to the polymer insulator was. It can be determined that the polymer insulator mounted on a live part must be examined when the discharge risk exceeds approximately 40%. In conclusion, the status of power utilities can be checked using an UV sensor.

• Proceedings of the KIEE Conference
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• 2003.07c
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• pp.1797-1799
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• 2003

In this paper, we measured partial discharge inception voltage(PDIV) and frequency spectra of UHF signal under following three cases, (1) a corona discharge due to a non uniform electric field for needle-plate electrode (2) a surface discharge caused by particles attached to a spacer in GIS, (3) free movements of particle in GIS. As a result of the experiment, it was revealed that the PDIV of Case (3) is higher than PDIVs of Case (1) and Case (2). It was also seem that UHF signal frequency spectra of one case are different from those of the other cases.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.1 no.3
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• pp.119-125
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• 2008

This work involves analyzing partial discharge (PD), which has estimated the detected signal accumulation based on Labview, and analyzing by Fuzzy algorithm. In algorithm, we developed system configuration that detected accumulating PD signal. With practical PD logic implementation of theoretical detected system and hardware implementation, the device for 50kV setup has generated and then has applied with 15k~17kV with 1:1 time probe. Our new class of PD detected algorithm has also compared with PRPDA or Fuzzy algorithm, which has diagnose more conveniently by adding numerical values.

• Proceedings of the KIEE Conference
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• 1993.07b
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• pp.588-590
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• 1993

This paper presents a diagnostic technique using ultrasonic for operating power transformer. Two methods are used as a base for detecting a partial discharge. One is a analysis of PD trend using counted ultrasonic signal, the other is a estimation of PD location source using cross-correlation. In this paper we implement detection equipment of partial discharge in power transformer. The desired system is utilized to two methods for PD detection and operated by graphical user interface(GUI). The results of test showed that the system can be adopted to real power transformer.

• KIEE International Transactions on Power Engineering
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• v.3A no.4
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• pp.214-221
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• 2003

In view of the fact that gas insulated switchgear (GIS) is an important piece of equipment in a substation, it is highly desirable to continuously monitor the state of equipment by measuring the partial discharge (PD) activity in a GIS, as PD is a symptom of an insulation weakness/breakdown. However, since the PD signal is relatively weak and the external noise makes detection of the PD signal difficult, it therefore requires careful attention in its detection. In this paper, the algorithm for detection of PD in the GIS using the wavelet transform (WT) is proposed. The WT provides a direct quantitative measure of the spectral content and dynamic spectrum in the time-frequency domain. The most appropriate mother wavelet for this application is the Daubechies 4 (db4) wavelet. 'db4', the most commonly applied mother wavelet in the power quality analysis, is very well suited to detecting high frequency signals of very short duration, such as those associated with the PD phenomenon. The proposed algorithm is based on utilizing the absolute sum value of coefficients, which are a combination of D1 (Detail 1) and D2 (Detail 2) in multiresolution signal decomposition (MSD) based on WT after noise elimination and normalization.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.29 no.2
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• pp.114-119
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• 2016

This paper dealt with the propagation characteristics of acoustic signal in insulation oil for the purpose of improving the reliability of AE (acoustic emission) method used for condition monitoring of oil-immersed transformers. A discharge source was placed in insulation oil and AE sensors ($f_c$ :140 kHz) were attached on the oil tank to study the changes of velocity and propagation path with the depth and distance. The average velocity was 1,436 m/s and the velocity decreased with the increase of depth from the oil surface to 430 mm. The propagation paths were classified into three sections by the shortest reflection path of the detected signal. The minimum distinguishable distance in each section was 70 mm. It was also verified that PD (partial discharge) with a magnitude over than 500 pC can be detected by the AE sensors.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.10
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• pp.2000-2004
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• 2009

In general, for the condition monitoring of a power transformer using the UHF PD measuring technique, detection of any partial discharge, identifying the defect in the transformer and locating the insulation defect are necessary. In this paper one of the most frequent detects which can result in turn to turn fault in power transformer was examined for identifying the defect. In order to model the defect, as a discharge source, a partial discharge cell was used for experimental activity. Magnitude of electromagnetic wave signals and corresponding amount of apparent discharge were measured simultaneously against phase of applied voltage to the discharge cell. Frequency range and phase resolved partial discharge signals were measured and analyzed. The results will be contributed to build the defect database of power transformer and to decrease the occurrence of transformer faults.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 1997.11a
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• pp.451-455
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• 1997

The partial discharge has been blown as the chief breakdown of power equipments. The analysis and the recognition is much difficult because the partial discharge signal is very small and has complex aging pattern. Recently, insulation aging diagnosis based on pattern of phase(Ф), partial discharge magnitude(q), number(n) has been very important. Owing to depreciate the reappearance of aging progress at the electrical tree pattern and to be difficult to analyze visually, the study on partial discharge pattern is suggested to normalizing analysis method of partial discharge signals. This parer is purposed on prediction of life-time measurement of cv-cable, on decision of risk degree with normalization and real-time measurement of partial discharge signals for aging diagnosis of cv-cable. As normalizing the aging signals of electrical tree in cv-cable, it is able to confirm risk degree of insulation material with the distribution of Ф-q-n and recognize the process of aging pattern using neural network.

• Proceedings of the KIEE Conference
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• 2008.09a
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• pp.224-226
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• 2008

In this paper, we developed HPF (high pass filter) consisted of lumped LC components to decrease the noises that are detected in PD (partial discharge) measuring test, and adapted it to field test.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.20 no.1
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• pp.65-73
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• 2007

Pattern classification of single and multiple discharge sources was applied using a wavelet image signal method in which a feature extraction was applied using a hidden sub-image. A feature extracting method that used vertical and horizontal images using an MSD method was applied to an averaging process for the scale of pulses for the phase. A feature extracting process for the preprocessing of the input of a neural network was performed using an inverse transformation of the horizontal, vertical, and diagonal sub-images. A back propagation algorithm in a neural network was used to classify defective signals. An algorithm for wavelet image processing was developed. In addition, the defective signal was classified using the extracted value that was quantified for the input of a neural network.