Partial discharge. IX. PD in gas-insulated substations-fundamental considerations

Baumgärtner, R.; Fruth, B.; Lanz, W.; Pettersson, Kjell

doi:10.1109/57.108819

Cited by 49 publications

(11 citation statements)

References 12 publications

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“…It is well known that insulation breakdown is often preceded by PD activities, so the detection of PD is very important to early detection of insulation defects and increases the reliability of the power supply [2][3][4]. Wide band PD measurement in the UHF range offers a better Signal to Noise Radio (SNR) than the conventional method, which has been in use widely.…”

Section: Introductionmentioning

confidence: 99%

Classification of UHF Partial Discharge in GIS Using Optimal Complex Wavelet-based Features

Zhou¹,

Liu²,

Tang³

2008

2008 International Conference on High Voltage Engineering and Application

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In the field of electrical power engineering, pattern recognition is commonly used for classification of defects caused of Partial Discharge (PD) in Gas Insulated Substation (GIS) in order to assess the insulated system in time. As phase information can't be acquired effectively using real wavelet transform for extracting features because of only real coefficients being existed, in this paper, the method of complex wavelet transform (CWT) for decomposing ultra-high frequency (UHF) PD signals is proposed; complex coefficient is constructed by combining the real part and imaginary part of complex wavelet coefficients which can describe PD signal integrally; and the features can be extracted from complex coefficients of each scale; additionally, optimal complex coefficients are selected by comparing Jsum criterion; Finally, PD samples are acquired through large number of experiments, and BP neural network is used to identify the PD sourced by four typical insulated defects effectively. The results show that the features extracted from the optimal compound coefficient corresponding to the best complex wavelet have wonderful results. I INTRODUCTIONGIS has developed very quickly and has been in use all over the world since the early seventies [1]. It is well known that insulation breakdown is often preceded by PD activities, so the detection of PD is very important to early detection of insulation defects and increases the reliability of the power supply [2][3][4]. Wide band PD measurement in the UHF range offers a better Signal to Noise Radio (SNR) than the conventional method, which has been in use widely.In the past, there were three different categories of PD pulse data patterns such as phase-resolved data, time-resolved data, and frequency-resolved data [5]. With the fast development of the computer technology, wavelet transform have attracted much attention in PD pattern studies. As phase information can't be acquired effectively using real wavelet transform for extracting features because of only real coefficients being existed, in this paper, the method of CWT for decomposing UHF PD signals is proposed, and complex coefficient n I R is constructed by combining the real part and imaginary part of complex wavelet coefficients which can describe PD signal integrally; and the features can be extracted from complex coefficients of each scale using cluster analysis; additionally, optimal complex coefficients are selected by comparing J sum criterion; Finally, PD samples are acquired through large number of experiments, and BP neural network based on chaos and tabu search optimization algorithm is used to identify the PD sourced by four typical insulated defects effectively. The results show that the features extracted from the optimal compound coefficient corresponding to the best complex wavelet have wonderful results. II PD FEATURES BASED ON COMPLEX COEFFICIENT A. Construction of Complex CoefficientsAt present, the method for extracting features from wavelet coefficients mainly includes parts of wavelet coefficient...

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Section: Introductionmentioning

confidence: 99%

Classification of UHF Partial Discharge in GIS Using Optimal Complex Wavelet-based Features

Zhou¹,

Liu²,

Tang³

2008

2008 International Conference on High Voltage Engineering and Application

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show abstract

“…Failures that occur in GIS often result from electrical breakdown in the insulation material, this may occur because of insulation damage caused by the cumulative adverse effects of partial discharge (PD) events. So the detection of PD is very important to early detection of insulation defects and increases the reliability of the power supply [1][2][3]. Wide band PD measurements in the ultrahigh-frequency (UHF) range offer a better signal to noise ratio (SNR) than the conventional method.…”

Section: Introductionmentioning

confidence: 99%

Feature extraction and recognition of UHF partial discharge signals in GIS based on dual‐tree complex wavelet transform

Tang

Zhou

2009

Int Trans Elec Energy Syst

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SUMMARYUltra-high-frequency (UHF) partial discharge (PD) detection is an effective means for evaluation of dielectric condition of gas insulated switchgear (GIS). Time-resolved data pattern is utilized to recognize insulation defect because of its attractive advantages that there exists some direct relationship between the shape of PD signal and the physics of the defect. Therefore, the main task is feature extraction of time-resolved data. However, it is hard to extract features in time domain because there is no obvious difference in types of signals. UHF PD signals are non-stationary, so feature extraction in frequency domain is not suitable as well. Feature extraction in time-frequency domain based on discrete wavelet transform (DWT) is a solution for this problem, but there is a weak point which hampers the application of this method, that is, lack of shift invariance, which means that small shifts in the input signal can cause major variations in the distribution of energy between DWT coefficients at different scales. In this paper, a new method of feature extraction of UHF PD signal on the basis of dual-tree complex wavelet transform (CWT) which can reduce shift sensitive is proposed. UHF PD signals are acquired in lab and decomposed by a dual-tree CWT. Then the feature space is constructed through the energy on all frequency bands and modulus maxima on all scales. At last feature space is inputted into a radial base function (RBF) neural network classifier. The results show the superiority of this method over traditional ones.

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“…As insulation failure usually starts with partial discharge (PD) activity; several studies have been performed to use PD measurements as a diagnostic method for detecting defects and preventing major insulation breakdown [1,2]. PD activity in GIS can arise from protrusions on the conductor, free conducting particles, particles fixed on spacer, floating components and spacer defects such as void and detachment as illustrated in Fig.…”

Section: Introductionmentioning

confidence: 99%

Comparison of partial discharge characteristics for different defect types in SF<inf>6</inf> gas insulation system

Mansour

Okusu

Nishizawa

et al. 2008

2008 12th International Middle-East Power System Conference

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The high reliability, less maintenance and compact size of Gas Insulated Switchgears (GISs) have made them the primary choice for many utilities. However, sometimes insulation defects inside GIS can be a serious threat to safe operation of GIS and can lead to costly disruption of supply. As insulation failure usually starts with partial discharge (PD) activity, this paper investigates the differences in PD characteristics in SF 6 gas among different types of defects. The defect types considered in this study are particles in a gas gap, particles adhered on a spacer surface and spacer/electrode detachment. Different experiments were made for sequential PD measurements using the system of PD-Current Pulse Waveform Analysis (PD-CPWA). The PD phase characteristics, PD pulse number and PD current were analyzed for the different defect types. Also the ratio of voltage increment to phase increment at the next PD pulse appearance (∆u/∆φ pattern) was obtained and compared for each defect type. Experimental results show that correct identification of defects can be achieved based on considered PD characteristics.

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Partial discharge. IX. PD in gas-insulated substations-fundamental considerations

Cited by 49 publications

References 12 publications

Classification of UHF Partial Discharge in GIS Using Optimal Complex Wavelet-based Features

Classification of UHF Partial Discharge in GIS Using Optimal Complex Wavelet-based Features

Feature extraction and recognition of UHF partial discharge signals in GIS based on dual‐tree complex wavelet transform

Comparison of partial discharge characteristics for different defect types in SF<inf>6</inf> gas insulation system

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