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

Mas’ud, Abdullahi Abubakar and Albarracin Sanchez, Ricardo and Ardila-Rey, Jorge Alfredo and Muhammad-Sukki, Firdaus and Illias, Hazlee Azil and Bani, Nurul Aini and Munir, Abu Bakar (2016). Artificial Neural Network Application for Partial Discharge Recognition: Survey and Future Directions. "Energies", v. 9 (n. 8); p. 574. ISSN 1996-1073. https://doi.org/10.3390/en9080574.

Description

Title: Artificial Neural Network Application for Partial Discharge Recognition: Survey and Future Directions
Author/s:
  • Mas’ud, Abdullahi Abubakar
  • Albarracin Sanchez, Ricardo
  • Ardila-Rey, Jorge Alfredo
  • Muhammad-Sukki, Firdaus
  • Illias, Hazlee Azil
  • Bani, Nurul Aini
  • Munir, Abu Bakar
Item Type: Article
Título de Revista/Publicación: Energies
Date: 2016
ISSN: 1996-1073
Volume: 9
Subjects:
Freetext Keywords: partial discharge (PD); artificial neural network (ANN); artificial intelligence
Faculty: E.T.S.I. Diseño Industrial (UPM)
Department: Ingeniería Eléctrica, Electrónica Automática y Física Aplicada
Creative Commons Licenses: None

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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.

More information

Item ID: 45425
DC Identifier: http://oa.upm.es/45425/
OAI Identifier: oai:oa.upm.es:45425
DOI: 10.3390/en9080574
Official URL: http://www.mdpi.com/1996-1073/9/8/574/pdf
Deposited by: Ricardo Albarracin Sanchez
Deposited on: 06 Apr 2017 07:53
Last Modified: 06 Apr 2017 07:54
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