Pattern recognition of partial discharge in XLPE cables using a neural network

Suzuki, H.; Endoh, Takeshi

doi:10.1109/icpadm.1991.172350

Cited by 39 publications

(13 citation statements)

References 3 publications

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“…These fractal features were further used as characteristic parameters. Another approach is reported in [35,36], where the 3-dimensional discharge magnitude-phase histogram H ( q , p) is directly used as a set of characteristic parameter. The classification is made by neural networks [35,36].…”

Section: Ac Discharge Diagnosticsmentioning

confidence: 99%

Interpretation of partial discharges at dc voltages

Fromm

1995

IEEE Trans. Dielect. Electr. Insul.

113

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The aim of this paper is to give an overview on partial discharges at d c voltage. A model is presented which describes the stochastic discharge process. The model is experimentally verified for internal discharges and for corona in air. In addition, the classification of discharge patterns is discussed. The 3-dimensional histogram describing the number of discharges depending on the discharge magnitude and on the time to the successive discharge is suggested to be used as a base for discharge recognition at d c voltage. Experimental proof is presented to show the strength of this approach.

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Section: Ac Discharge Diagnosticsmentioning

confidence: 99%

Interpretation of partial discharges at dc voltages

Fromm

1995

IEEE Trans. Dielect. Electr. Insul.

113

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“…This result clearly indicates that smaller numbers of pixels in the ϕ-q-n distributions are better PD recognition parameters for the ANN. The technique of choosing learning fingerprints by Suzuki and Endoh [49] was adopted by Hozumi et al [50] and Phung et al [51]. They also applied the BP algorithm and the result shows that the ANN learns and updates faster with high recognition rate above 90%.…”

Section: Relevant Previous Research Work On Artificial Neural Networmentioning

confidence: 99%

“…The initial stage in pattern recognition was the choice of appropriate fingerprints that can be applied as training and testing parameters for the ANN. Earlier research work by Suzuki and Endoh [49] showed how the φ-q-n patterns from a needle-type defect in cross-linked polyethylene (XLPE) cable are transformed into smaller patterns by reducing the number of pixels, thereby minimizing the number of amplitude and phase resolutions. This is to ensure reduction of the input data to the ANN.…”

Section: Relevant Previous Research Work On Artificial Neural Networmentioning

confidence: 99%

“…They also applied the BP algorithm and the result shows that the ANN learns and updates faster with high recognition rate above 90%. Gulski and Krivda [10] evaluated the performance of different ANN algorithms, though they used a different approach in determining the input pattern when compared to that of Suzuki and Endoh [49].…”

Section: Relevant Previous Research Work On Artificial Neural Networmentioning

confidence: 99%

“…‚ Very few works have considered the application of ANNs for discriminating PD source positioning [49]. Discharges from similar PD sources (e.g., corona, void, surface discharges) at various positions of the HV insulation may vary in characteristics depending on whether it is single or multiple sources.…”

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confidence: 99%

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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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Prediction of tree initiation by analysis of discharge magnitude and discharge luminescence at each phase angle section

Ehara

Fukawa

Kishida

et al. 2003

Electrical Engineering Japan

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SUMMARYPartial discharge is one of the causes of deterioration of insulating polymer materials subjected to high voltages. In this case, the deterioration that is caused by partial discharges in gaps, such as voids inside an insulator, is of particular importance. In this study, the prediction of tree initiation due to partial discharge occurring in voids is considered. New tree initiation diagnostic methods are proposed by measuring discharge magnitude and discharge luminescence in each phase angle section of the applied voltage. It is noted that some samples revealed different time dependence of partial discharge patterns even under the same measurement condition. These patterns were the case of occurrence of swarming pulsive micro discharges (SPMDs) before tree initiation and the case of no occurrence of SPMDs. The prediction of tree initiation by using moving average of the discharge magnitude is discussed. We propose the parameters to reflect a pre-tree initiation mode and also evaluate their usefulness for the prediction of tree initiation by using moving average analysis.

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Pattern recognition of partial discharge in XLPE cables using a neural network

Cited by 39 publications

References 3 publications

Interpretation of partial discharges at dc voltages

Interpretation of partial discharges at dc voltages

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

Prediction of tree initiation by analysis of discharge magnitude and discharge luminescence at each phase angle section

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