Comparison of the Performance of Artificial Neural Networks and Fuzzy Logic for Recognizing Different Partial Discharge Sources

Abdullahi Abubakar Mas’ud, Jorge Alfredo Ardila-Rey, Ricardo Albarracín, Firdaus Muhammad-Sukki and Nurul Aini Bani
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Abdullahi Abubakar Mas’ud: Department of Electrical and Electronics Engineering, Jubail Industrial College, P.O. Box 10099, Jubail 31961, Saudi Arabia
Jorge Alfredo Ardila-Rey: Department of Electrical Engineering, Universidad Técnica Federico Santa María, Santiago de Chile 8940000, Chile
Ricardo Albarracín: Departamento de Ingeniería Eléctrica, Electrónica, Automática y Física Aplicada, Escuela Técnica Superior de Ingeniería y Diseño Industrial, Universidad Politécnica de Madrid, Ronda de Valencia 3, 28012 Madrid, Spain
Firdaus Muhammad-Sukki: School of Engineering, Robert Gordon University, Aberdeen AB10 7GJ, UK
Nurul Aini Bani: UTM Razak School of Engineering and Advanced Technology, Universiti Teknologi Malaysia, 54100 Kuala Lumpur, Malaysia

Energies, 2017, vol. 10, issue 7, 1-20

Abstract: This paper compared the capabilities of the artificial neural network (ANN) and the fuzzy logic (FL) approaches for recognizing and discriminating partial discharge (PD) fault classes. The training and testing parameters for the ANN and FL comprise statistical fingerprints from different phase-amplitude-number ( ?-q-n ) measurements. Two PD fault classes considered are internal discharges in voids and surface discharges. In the void class, there are single voids, serial voids and parallel voids in polyethylene terephthalate (PET), while the surface discharge class comprises four different surface discharge arrangements on pressboard in oil at different voltages and angular positioning of the ground electrode on the respective pressboards. Previously, the ANN and FL have been investigated for PD classification, but there is no work reported in the literature that compares their performance, specifically when applied for real time PD detection problem. As expected, both the ANN and FL can recognize PD defect classes, but the results show that the ANN appears to be more robust as compared to the FL, but these conclusions required to be further investigated with complex PD examples. Finally, both the ANN and FL were assessed as practical PD classification. Despite of the limitations of the ANN, it is concluded that the ANN is better suited for practical PD recognition because of its ability to provide accurate recognition values and the severity level of PD defects.

Keywords: partial discharge; fuzzy logic; artificial neural network; polyethylene terephthalate (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (10)

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