• 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.

• Proceedings of the Acoustical Society of Korea Conference
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• 1992.06a
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• pp.87-90
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• 1992

Detecting acoustic emission (AE) provides an appropriate method to diagonize on-line transformers, since acoustic signal is not influenced by strong electric field. Then AE versus AE signal processing method is investigated. But this processing is difficult that decision of starting point of AE wave with acoustic noise. This problem is sloved by correlation which calculate time interval between two signals eactly. This paper presents a technique locating the eact position of the partial discharge (PD) in a power transformer by the correlation of the AE signals from two ultrasonic sensors. And PD position is displayed on monitor. Laboratory tests confirmed that the proposed method can be used for locating the PD in power transformer.

• The Proceedings of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.9 no.6
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• pp.65-70
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• 1995

This paper describes the detection of the ultrasonic signals reduced by materials of a transformer for diagnosis of the transformer using ultrasonic signal which is generated by partial discharge. When partial discharge is generated on the surface of the winding and between the winding and the core in the transformer, the ultrasonic signal can be measured as the proper selection of the ultrasonic detectors' location.

• Proceedings of the KIEE Conference
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• 2000.11a
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• pp.136-138
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• 2000

This paper describes an efficient location detection algorithm for a partial discharge(PD) source in the transformer. The algorithm is previously proposed is not suitable for PD source detection because ultrasonic signal is diminished through the inner structure. In this paper, the proposal algorithm find PD source using geometry method and 3th sensor on the one side of transformer without diminution of ultrasonic signal. The proposed algorithm demonstrates the effectiveness and validity on model transformer.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 1999.11a
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• pp.299-301
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• 1999

This paper describes the characteristics of PD pattern and acoustic signal for the possible four states of particles in GIS. For each case of defect, the PD patterns and acoustic signals were measured and analyzed. As a result, the characteristics of the discharge patterns and ultrasonic signals depending for each defect state could be distinguished. Using the discharge patterns and ultrasonic signal waves, the defects in the GIS and the states of the particles can be distinguished.

• Journal of Electrical Engineering and Technology
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• v.9 no.4
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• pp.1375-1384
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• 2014

Flashover of power transmission line insulators is a major threat to the reliable operation of power system. This paper deals with the flashover prediction of polymeric insulators used in power transmission line applications using the novel condition monitoring technique developed by PD signal time-frequency map and neural network technique. Laboratory experiments on polymeric insulators were carried out as per IEC 60507 under AC voltage, at different humidity and contamination levels using NaCl as a contaminant. Partial discharge signals were acquired using advanced ultra wide band detection system. Salient features from the Time-Frequency map and PRPD pattern at different pollution levels were extracted. The flashover prediction of polymeric insulators was automated using artificial neural network (ANN) with back propagation algorithm (BPA). From the results, it can be speculated that PD signal feature extraction along with back propagation classification is a well suited technique to predict flashover of polymeric insulators.

• Proceedings of the KIEE Conference
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• 2004.11a
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• pp.255-257
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• 2004

This paper was studied electromagnetic field distribution and damping of PD(Partial Discharge) signal in GIS(Gas Insulated Switchgear). Cut-off frequency of electromagnetic wave propagation modes were computed, electromagnetic field distribution of propagation modes in GIS by FEM(Finite Element Method) were simulated and simulated damping characteristic of electromagnetic waves in GIS by EMTP(Electromagnetic Transient Program) when generated PD pulse. Frequency band of $TE_{mn}/TM_{mn}$ modes were determinated by simulation results of electromagnetic field distribution and were discussed optimal position of UHF sensor from this results. Equivalent circuit was used to simulate signal damping of PD pulse in GIS by EMTP and compared with measured results in laboratory of KERI.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.20 no.10
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• pp.907-911
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• 2007

This paper dealt with the frequency spectrum analysis of acoustic signal produced by partial discharge (PD) in insulation oil and the positioning of PD occurrence to apply in diagnosis of oil insulated transformers. Three types of electrode system ; the needle-plane, the plane-plane, and the wire-wire structure were assembled to simulate partial discharges in oil insulated transformers. A low-noise amplifier and a decoupling circuit were designed to detect acoustic signal with high sensitivity The frequency spectrum of the acoustic signal were 50 kHz ${\sim}$ 260 kHz in the needle-plane, 50 kHz ${\sim}$ 250 kHz in the plane-plane, and 45 kHz${\sim}$195 kHz in the wire-wire electrode system. Their peak frequencies were 145 kHz, 130 kHz and 114 kHz, respectively The position of PD occurrence was calculated by the time difference of arrival (TOA) using three acoustic emission (AE) sensors, and we could find the position within the error of 1 % in the experimental apparatus.

• Journal of Electrical Engineering and Technology
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• v.11 no.1
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• pp.192-199
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• 2016

With changes in insulated defects, the environment, and so on, new partial discharge (PD) data are highly different from the original samples. It leads to a decrease in on-line recognition rate. The UHF signal and pulse current signal of four kinds of typical artificial defect models in gas insulated switchgear (GIS) are obtained simultaneously by experiment. The relationship map of ultra-high frequency (UHF) cumulative energy and its corresponding apparent discharge of four kinds of typical artificial defect models are plotted. UHF cumulative energy and its corresponding apparent discharge are used as inputs. The support vector machine (SVM) incremental method is constructed. Examples show that the PD SVM incremental method based on simulated annealing (SA) effectively speeds up the data update rate and improves the adaptability of the classifier compared with the original method, in that the total sample is constituted by the old and new data. The PD SVM incremental method is a better pattern recognition technology for PD on-line monitoring.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.56 no.12
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• pp.2184-2188
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• 2007

We developed a novel method of partial discharge(PD) location based on the fact that the waveform of PD signal propagate along the GIB (Gas Insulated Bus) is composed of several modes of electromagnetic wave with different group velocities and cut-off frequencies. From the PD waveform, measured at a broadband PD sensor attached on the GIB, we could derive arrival time and frequency components of different modes using the short term Fourier transform or etc. After the group velocities of different modes are calculated, the location of the PD source could be estimated. To show the effectiveness of this new locating method in a real on site application, we used this method to locate the position of a PD source at a 76 m long 345 kV GIB substation. The estimated location of the PD source using the method proposed above was in good agreement with the actual location found from the inspection result of internal component in the GIB with 2.4% of the estimation error.