Feature extraction of partial discharge signals using the wavelet packet transform and classification with a probabilistic neural network

Evagorou, Demetres; Kyprianou, Andreas; Lewin, P.L.; Stavrou, Andréas; Efthymiou, Venizelos; Metaxas, Alex; Georghiou, George E.

doi:10.1049/iet-smt.2009.0023

Cited by 132 publications

(89 citation statements)

References 30 publications

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“…Thus the feature extraction for multi PD source separation has become the most recently research hotspot. The scholars proposed a variety of feature extraction methods based on pulse waveform [65,[97][98][99][100][101][102][103][104][105][106][107], which were proved to be promising in multi PD source separation by on-site testing as well as laboratory experiments. Table 6 summarizes feature extraction methods presented in this chapter, the corresponding reference is attached.…”

Section: Discussionmentioning

confidence: 99%

“…For example, Day et al [82] applied CWT on detected noisy PD waveforms and that superposed on power frequency signal, then extracted the first seven statistical moments from cross-wavelet spectrum to form feature vector. Similarly, in the literature [107], the peak-time series measured by the peak detection technique was decomposed by wavelet packet transform (WPT) to nine levels. Then the first four statistical moments of the probability density function of the wavelet coefficients at all nine scales were extracted to form feature vectors.…”

Section: Signal Processingmentioning

confidence: 99%

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A Review of Online Partial Discharge Measurement of Large Generators

Luo

Wang³

2017

Energies

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Abstract:Online partial discharge (PD) measurements have long been used as an effective means to assess the condition of the stator windings of large generators. An increase in the use of PD online measurement systems during the last decade is evident. Improvements in the detection capabilities are partly the reason for the increased popularity. Another reason has been the development of digital signal processing techniques. In addition, rapid progress is being made in automated single PD source classification. However, there are still some factors hindering wider application of the system, such as the complex PD mechanism and PD pulse propagation in stator windings, the presence of detrimental noise and disturbances on-site, and multiple PD sources occurring simultaneously. To avoid repetition of past work and to provide an overview for fresh researchers in this area, this paper presents a comprehensive survey of the state-of-the-art knowledge on PD mechanism, PD pulse propagation in stator windings, PD signal detection methods and signal processing techniques. Areas for further research are also presented.

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Section: Discussionmentioning

confidence: 99%

Section: Signal Processingmentioning

confidence: 99%

A Review of Online Partial Discharge Measurement of Large Generators

Luo

Wang³

2017

Energies

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“…Recently it has also been applied for extracting representative features of different PD patterns corresponding to various insulation defects in HV equipment [9].…”

Section: A Discrete Wavelet Transform (Dwt) Approachmentioning

confidence: 99%

Bayesian neural network and discrete wavelet transform for partial discharge pattern classification in high voltage equipment

Chan

Saha

2013

2013 IEEE Power &Amp; Energy Society General Meeting

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“…Evagorou et al [60] applied the PNN to categorize some PD fault geometries, i.e., corona and surface PD in air and oil. After training the PNN algorithm, the input vectors containing the features for classification were then applied to calculate the PDF of each category and collectively by assigning the cost for misclassification; the result minimizes the likely risk taken.…”

Section: Relevant Previous Research Work On Artificial Neural Networmentioning

confidence: 99%

Artificial Neural Network Application for Partial Discharge Recognition: Survey and Future Directions

et al. 2016

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Abstract:In order to investigate how artificial neural networks (ANNs) have been applied for partial discharge (PD) pattern recognition, this paper reviews recent progress made on ANN development for PD classification by a literature survey. Contributions from several authors have been presented and discussed. High recognition rate has been recorded for several PD faults, but there are still many factors that hinder correct recognition of PD by the ANN, such as high-amplitude noise or wide spectral content typical from industrial environments, trial and error approaches in determining an optimum ANN, multiple PD sources acting simultaneously, lack of comprehensive and up to date databank of PD faults, and the appropriate selection of the characteristics that allow a correct recognition of the type of source which are currently being addressed by researchers. Several suggestions for improvement are proposed by the authors include: (1) determining the optimum weights in training the ANN; (2) using PD data captured over long stressing period in training the ANN; (3) ANN recognizing different PD degradation levels; (4) using the same resolution sizes of the PD patterns when training and testing the ANN with different PD dataset; (5) understanding the characteristics of multiple concurrent PD faults and effectively recognizing them; and (6) developing techniques in order to shorten the training time for the ANN as applied for PD recognition Finally, this paper critically assesses the suitability of ANNs for both online and offline PD detections outlining the advantages to the practitioners in the field. It is possible for the ANNs to determine the stage of degradation of the PD, thereby giving an indication of the seriousness of the fault.

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Feature extraction of partial discharge signals using the wavelet packet transform and classification with a probabilistic neural network

Cited by 132 publications

References 30 publications

A Review of Online Partial Discharge Measurement of Large Generators

A Review of Online Partial Discharge Measurement of Large Generators

Bayesian neural network and discrete wavelet transform for partial discharge pattern classification in high voltage equipment

Artificial Neural Network Application for Partial Discharge Recognition: Survey and Future Directions

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