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Artificial neural network application for partial discharge recognition: survey and future directions.

Mas'ud, Abdullahi Abubakar; Albarrac�n, Ricardo; Ardila-Rey, Jorge Alfredo; Muhammad-Sukki, Firdaus; Illias, Hazlee Azil; Bani, Nurul Aini; Munir, Abu Bakar


Abdullahi Abubakar Mas'ud

Ricardo Albarrac�n

Jorge Alfredo Ardila-Rey

Firdaus Muhammad-Sukki

Hazlee Azil Illias

Nurul Aini Bani

Abu Bakar Munir


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.


MAS'UD, A.A., ALBARRACIN, R., ARDILA-REY, J.A., MUHAMMAD-SUKKI, F., ILLIAS, H.A., BANI, N.A. and MUNIR, A.B. 2016. Artificial neural network application for partial discharge recognition: survey and future directions. Energies [online], 9(8), article number 574. Available from:

Journal Article Type Article
Acceptance Date Jul 15, 2016
Online Publication Date Jul 25, 2016
Publication Date Aug 31, 2016
Deposit Date Aug 24, 2016
Publicly Available Date Aug 24, 2016
Journal Energies
Electronic ISSN 1996-1073
Publisher MDPI
Peer Reviewed Peer Reviewed
Volume 9
Issue 8
Article Number 574
Keywords Partial discharge (PD); Artificial neural network (ANN); Artificial intelligence
Public URL


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