Application of data mining on partial discharge part I: predictive modelling classification

Lai, K.X.; Phung, B.T.; Blackburn, T.R.

doi:10.1109/tdei.2010.5492258

Cited by 53 publications

(27 citation statements)

References 26 publications

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“…In [88], the PD signals were represented by n-ϕ, q a -ϕ and q m -ϕ, Ma et al extracted seven principal components by PCA as well as seven elements by SNE to form the features set. Besides above 3 PD patterns, a new average discharge current i versus ϕ was introduced by Lai et al [89] for feature extraction by PCA. Differing from that, Raymond et al [90] first split the n-q-ϕ profile into six groups, including negative and positive discharge groups and four phase quadrants groups.…”

Section: Pca For Pd Distributionmentioning

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: Pca For Pd Distributionmentioning

confidence: 99%

“…All the approaches [88][89][90] used PCA to extract features and reduce dimensionality. Their difference is at the PD patterns used for extraction.…”

Section: Pca For Pd Distributionmentioning

confidence: 99%

A Review of Online Partial Discharge Measurement of Large Generators

Luo

Wang³

2017

Energies

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“…The PNN obtains the probability density function (PDF) based on Bayes' decision making approach [18,28]. The PNN is made up of input layer, hidden layer and output layer [27]. The hidden layer consists of the exemplar and summation layers.…”

Section: The Probabilistic Neural Networkmentioning

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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“…For this reason, detecting partial discharge is useful for the insulation assessment and to predict the life of the insulation [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

A New Reliable Algorithm for Identifying Types of Partial Discharge Detected through Ultrasonic Emission

Hapeez

Hamzah

Hashim

et al. 2014

Journal of Electrical Engineering and Technology

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-This paper presents a simple, consistent and reliable technique to identify detected partial discharges (PD) using an acoustic ultrasonic method. A new reliable algorithm named 'Simple Partial Discharge Identifier' (SPDI) is proposed to perform identification process of the detected ultrasonic signals of PD. Experimental works based on recommended practices were setup and the ultrasonic signals of the PD were recorded. The PD data is then employed as the reference data. The SPDI developed has been tested against commonly used models in Neural Network (NN). Results from the SPDI algorithm shows more reliable results compared to NN models results. Comparison made on the mean square error (MSE) results shows SPDI produces the desired outcome with lower MSE in 97.17% of trials. Low error of SPDI indicates a high reliability to be applied in the identification of PD.

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Application of data mining on partial discharge part I: predictive modelling classification

Cited by 53 publications

References 26 publications

A Review of Online Partial Discharge Measurement of Large Generators

A Review of Online Partial Discharge Measurement of Large Generators

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

A New Reliable Algorithm for Identifying Types of Partial Discharge Detected through Ultrasonic Emission

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