• KIEE International Transactions on Electrophysics and Applications
• /
• v.3C no.5
• /
• pp.177-181
• /
• 2003

Partial discharge pulse shapes from variable PD (partial discharge) sources sustain many characteristics such as types of PD. Ultra high frequency antennas have wide bandwidth from 30KHz to 2㎓. Therefore, signals taken from a UHF antenna have important attributes (rising time, falling time, shape factor, etc.) for electromagnetic sources, such as PD sources. We investigated PD pulse shapes from several PD sources using a UHF antenna and the results were used for classification of PD sources. Features for discrimination are extracted from frequency distribution and LPC (Linear Prediction Coefficient) of time signal. RBFN are used for investigating the possibility of classification of multi-PD sources.

• The Transactions of the Korean Institute of Electrical Engineers C
• /
• v.54 no.11
• /
• pp.508-514
• /
• 2005

In this paper, it was investigated the characteristics of PD(partial discharge) pulse shapes in oil and the possibility of TF(Time-Frequency) analysis for the discrimination of air corona in on-site PD measurement of oil transformers. For the purpose, single and multiple discharges combined with oil discharges and air corona were generated and measured by use of artificial models through the HFPD pulse detector and the oscilloscope. PD pulse shapes were different according to the type of defects, especially including air corona- pulses. Also, through the TF analysis, data clusters could be classified each other in the TF Map, even in the case of multiple discharges. Therefore, it is known that TF analysis could be aprlied for the improvement of on-site PD measurement in oil transformers.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
• /
• 1999.05a
• /
• pp.704-707
• /
• 1999

Partial discharge(PD) in air insulated electric power systems is responsible for considerable power lossesfrom high voltage transmission lines. PD in air often leads to deterioration of insulation by the combined action of the discharge ions bombarding the surface and the action of chemical compounds that are formed by the discharge and may give rise to interference in ommunication systems. PD can indicate incipient failure. Thus understanding and classification of PD in air is very important to discern source of PD. In this paper, we investigated PD in air by using statical method. We classified air discharge with corona, surface discharge and cavity discharge by source of discharge. we used the mean pulse-height phase distribution $H_{qmean}(\psi)$, the max pulse-height phase distribution $H_{qmax}(\psi)$ , the pulse count phase distribution $H_n(\psi)$ and the max pulse height vs. repetition rate $H_{q}(n)$ for analysis PD pattern. We used statistical operators, such as skewness(S+. S-1, kurtosis(K+, K-), mean phase(AP+. AP-), cross-correlation factor(CC) and asymmetry from the distribution.

• Proceedings of the KSR Conference
• /
• 2004.10a
• /
• pp.1378-1383
• /
• 2004

Rogowski coil is a kind of sensor for high amplitude and very short period current and suitable for the measurement of PD pulse. This paper have simulated the relations between PD signals and Rogowski coil which has its own equivalent circuit and investigated signals of Rogowski coil induced by PD pulse current. and compared with the PD signal obtained from conventional PD measuring system in accordance with IEC60270.

• The Transactions of the Korean Institute of Electrical Engineers C
• /
• v.49 no.1
• /
• pp.19-23
• /
• 2000

Partial discharges(PD)in air insulated electric power apparatus often lead to deterioration of solid insulation by electron bombardments and electrochemical reaction. The PD caused to reduce the life time of power apparatus and to increase power losses. Thus understanding and classification of PD patterns in air are very important to discern sources of PD. In this paper, PD in air by using statistical methods was investigated. We classified air discharges, corona, surface discharges and cavity discharges by Kohonen network. For classification of PD patterns, we used statistical operators and parameters such as skewness$(S^+,\; S^-),\; kurtosis(K^+, K^-),\; mean phase(AP^+, AP^-)$, cross-correlation factor(CC) and asymmetry derived from the mean pulse-height phase distribution$(H_{avg}(\phi))$, the max pulse-height phase distribution $(H_{qmax}(\phi))$, the pulse count phase distribution $(H_n(\phi))$ and the pulse height vs. Repetition rate $(H_q(n))$.

• Proceedings of the KIEE Conference
• /
• 1997.07d
• /
• pp.1624-1626
• /
• 1997

In this paper, a new PD measurement technique of pulse superposition method was proposed in order to investigate both PD mechanism and phase related PD patterns. From the experimental results, we could find that the polarity and the phase angle of the superposed pulse take great effects on PD characteristics and these method make it possible to get the physical information which statistical approach can not give. We could conclude that the changes of PD characteristics in the void surrounded by polymers may be attributed to the injection of charge carriers thereby formation of space charge in the surface and/or the bulk of the polymer.

• The Transactions of the Korean Institute of Electrical Engineers C
• /
• v.54 no.7
• /
• pp.332-338
• /
• 2005

PD(Partial Discharge) signal emitted from PD sources has their intrinsic features in the region of time and frequency STFT(Short Time Fourier Transform) shows time-frequency distribution at the same time. 2-Dimensional matrices(33$\times$77) from STFT for PD pulse signals are a good feature vectors and can be decreased in dimension by wavelet 2D data compression technique. Decreased feature vectors(13$\times$24) were used as inputs of Back-propagation ANN(Artificial Neural Network) for discrimination of Multi-PD sources(air discharge sources(3), surface discharge(1)). They are a good feature vectors for discriminating Multi-PD sources in the air.

• Proceedings of the KIEE Conference
• /
• 2004.07c
• /
• pp.1784-1786
• /
• 2004

PD(Partial Discharge) signal emitted from PD sources has their intrinsic features in the region of time and frequency. STFT(Short Time Fourier Transform) shows time-frequency distribution at the same time. 2-Dimensional matrices(33${\times}$77) from STFT for PD pulse signals are a good feature vectors and can be decreased in dimension by wavelet 2D data compression technique. Decreased feature vectors(13${\times}$24) were used as inputs of Back-propagation ANN(Artificial Neural Network) for discrimination of Multi-PD sources(air discharge sources(3), surface discharge(1)). They are a good feature vectors for discriminating Multi-PD sources.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
• /
• v.28 no.5
• /
• pp.320-325
• /
• 2015

This paper dealt with a defect identification algorithm which is based on single partial discharge (PD) pulse analysis in gas insulated structure. Four types of electrode systems such as a needle-plane, a plane-needle, a free particle and a crack inside spacer were fabricated to simulate defects in gas insulated switchgear (GIS). We measured single PD pulse by an oscilloscope with a sampling rate of 5 GS/s and a frequency bandwidth of 1 GHz. Data aquisition and signal processing were controlled by a LabVIEW program. Physical shapes of PD pulses were compared with kurtosis, skewness and time-based parameters as rising time, falling time and pulse-width. These parameters were analysed by an algorithm with a back propagation algorithm (BPA). By applying the algorithm, the identification rate was 97% for the needle-plane electrode, 96% for the plane-needle electrode, 91% for the free particle and 93% for the crack inside spacer. The results verified that the algorithm could identify the type of defects in GIS.

• Journal of Electrical Engineering and Technology
• /
• v.9 no.2
• /
• pp.695-706
• /
• 2014

Partial discharge (PD) measurements have emerged as a dominant investigative tool for condition monitoring of insulation in high voltage equipment. But the major problem behind them the PD signal is severely polluted by several noises like White noise, Random noise, Discrete Spectral Interferences (DSI) and the challenge lies with removing these noise from the onsite PD data effectively which leads to preserving the signal for feature extraction. Accordingly the paper is mainly classified into two parts. In first part the PD signal is artificially simulated and mixed with white noise. In second part the PD is measured then it is subjected to the proposed denoising techniques namely Translation Invariant Wavelet Transform (TIWT). The proposed TIWT method remains the edge of the original signal efficiently. Additionally TIWT based denoising is used to suppress Pseudo Gibbs phenomenon. In this paper an attempt has been made to review the methodology of denoising the PD signals and shows that the proposed denoising method results are better when compared to other wavelet-based approaches like Fast Fourier Transform (FFT), Discrete Wavelet Transform (DWT), by evaluating five different parameters like, Signal to noise ratio, Cross-correlation coefficient, Pulse amplitude distortion, Mean square error, Reduction in noise level.