Wavelet packet denoising for online partial discharge detection in cables and its application to experimental field results

Kyprianou, Andreas; Lewin, P.L.; Efthimiou, Venizelos; Stavrou, Andréas; Georghiou, George E.

doi:10.1088/0957-0233/17/9/001

Cited by 47 publications

(31 citation statements)

References 40 publications

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“…Different CIs have various denoising effects and CI given in literature [3] nearly cannot extract the PD signals, because their SNR, NCC, VTP cannot meet the requirement. However, CI of WTRI n has better effect, for its NCC is bigger, VTP is more close to 1 and SNR runs up to 20 dB (shown in Table I).…”

Section: Suppression Of White Noisementioning

confidence: 99%

“…As the intense electromagnetic interferences are surrounded by the power equipments, such as generator units, power transformers [3], gas insulated lines (GIL)/GIS, and cables [4], the weak PD signals are buried in various noises or interferences. WT [2,[5][6][7] and wavelet packet [3][4] have obtained widespread application, especially for complex wavelet transform (CWT) proposed in the literature [1,[8][9][10] has not only the same mathematical principle of processing the non-stationary signal, but also it has better denoising effect.…”

Section: Introductionmentioning

confidence: 99%

“…WT [2,[5][6][7] and wavelet packet [3][4] have obtained widespread application, especially for complex wavelet transform (CWT) proposed in the literature [1,[8][9][10] has not only the same mathematical principle of processing the non-stationary signal, but also it has better denoising effect. The CWT has particular phase-frequency characteristic that it could adjust the phase-frequency characteristic to make it more adaptive to the signal.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Investigation of suppressing PD white noise by compound information of complex wavelet transform

Tang

Zhong-rong

Sun

2008

Int Trans Elec Energy Syst

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SUMMARYAccording to the distinct properties between the complex wavelet coefficients of partial discharge (PD) signals and white noises, this paper presents the specific compound information (CI) of WTRI n series for complex wavelet transform (CWT), which are utilized to suppress white noises. Through the simulation case, the influence of n-index value on denoising effect is discussed and the optimal CI of corresponding to different PD waveforms is analyzed by the simulation investigation. The parameters of variable tendency parameter (VTP), normalized correlation coefficient (NCC), and signal-to-noise ratio (SNR) are defined to synthetically evaluate the denoising effect. The db4 complex wavelet is constructed to suppress the white noise in ultra high frequency (UHF) PD detection system. The results indicate that the denoising effect of the CI for CWT is better than the simple information (SI) for CWT and the CI of WTRI n series have the optimal denoising effect to suppress white noise for UHF PD signals.

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Section: Suppression Of White Noisementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Investigation of suppressing PD white noise by compound information of complex wavelet transform

Tang

Zhong-rong

Sun

2008

Int Trans Elec Energy Syst

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“…Therefore, a potential approach to discriminate between different PD types, sources or locations is to combine both frequency and time domain analysis (e.g. through the use of wavelet packets [2]). …”

Section: Introductionmentioning

confidence: 99%

Partial discharge source discrimination using a support vector machine

Lewin

2010

IEEE Trans. Dielect. Electr. Insul.

147

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Partial discharge (PD) measurements are an important tool for assessing the health of power equipment. Different sources of PD have different effects on the insulation performance of power apparatus. Therefore, discrimination between PD sources is of great interest to both system utilities and equipment manufacturers. This paper investigates the use of a wide bandwidth PD on-line measurement system consisting of a radio frequency current transducer (RFCT) sensor, a digital storage oscilloscope and a high performance personal computer to facilitate automatic PD source identification. Three artificial PD models were used to simulate typical PD sources which may exist within power system apparatus. Wavelet analysis was applied to pre-process measurement data obtained from the wide bandwidth PD sensor. This data was then processed using correlation analysis to cluster the discharges into different groups. A machine learning technique, namely the support vector machine (SVM) was then used to identify between the different PD sources. The SVM is trained to differentiate between the inherent features of each discharge source signal. Laboratory experiments where the trained SVM was tested using measurement data from the RFCT as opposed to conventional measurement data indicate that this approach has a robust performance and has great potential for use with field measurement data.

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“…Good results were reported by Kyprianou et al [16] by introducing the Wavelet Packet Transform (WPT), but for a proper choice of the mother wavelet the authors considered PDs corrupted only by different levels of white noise, thus not including the great variety of noise and disturbances affecting partial discharge measurements. More recently Macedo et al [18] used the cross correlation factor as a unique performance parameter for the choice of an appropriate mother-wavelet, while in [17] Chang et al put in evidence that the possible reason for bad performances of the denoising techniques could be ascribed to a limited set of training signals or to an inadequate selection of the performing parameters.…”

mentioning

confidence: 99%

An Improved Methodological Approach for Denoising of Partial Discharge Data by the Wavelet Transform

Petrarca¹,

Lupo²

2014

PIER B

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Abstract-Partial Discharge (PD) measurements may be affected by external noise and disturbances of various natures such as interference from broadcasting stations, stochastic noise, pulses from power electronics, etc. Extracting PD pulses from such a noisy environment is therefore a crucial issue. This paper presents a wavelet based technique for automatic noise rejection. The core of the paper is the use of an improved methodological approach for the selection of a suitable wavelet, which aims at summing up the benefits and overcoming some limitations of previous techniques. Firstly, a very wide set of training signals is used for the identification of the decomposition level and for the calculation of suitable performance parameters that identify each wavelet; then a Performance Fingerprint is introduced in order to summarize the ability of a specific wavelet to reconstruct a partial discharge waveform, and a distance criterion is used for the selection of the most suitable wavelet. Afterwards, useful information is collected for the reconstruction of the PD signal, and finally, results on the application of the algorithm for a set of numerical and experimental signals are presented.

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Wavelet packet denoising for online partial discharge detection in cables and its application to experimental field results

Cited by 47 publications

References 40 publications

Investigation of suppressing PD white noise by compound information of complex wavelet transform

Investigation of suppressing PD white noise by compound information of complex wavelet transform

Partial discharge source discrimination using a support vector machine

An Improved Methodological Approach for Denoising of Partial Discharge Data by the Wavelet Transform

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