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Machines 2017, 5(3), 18; doi:10.3390/machines5030018

An Ensemble-Boosting Algorithm for Classifying Partial Discharge Defects in Electrical Assets

1
Department of Electrical and Electronics Engineering, Jubail Industrial College, P.O. Box 10099, Jubail Industrial City 31261, Saudi Arabia
2
Department of Electrical Engineering, Universidad Técnica Federico Santa María, Santiago 8940000, Chile
3
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, Madrid 28012, Spain
4
School of Engineering, Robert Gordon University, Aberdeen AB10 7GJ, Scotland, UK
*
Author to whom correspondence should be addressed.
Received: 18 July 2017 / Revised: 3 August 2017 / Accepted: 4 August 2017 / Published: 8 August 2017
(This article belongs to the Special Issue Machinery Condition Monitoring and Industrial Analytics)
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Abstract

This paper presents an ensemble-boosting algorithm (EBA) for classifying partial discharge (PD) patterns in the condition monitoring of insulation diagnosis applied for electrical assets. This approach presents an optimization technique for creating a sequence of artificial neural network (ANNs), where the training data for each constituent of the sequence is selected based on the performance of previous ANNs. Four different PD faults scenarios were manufactured in the high-voltage (HV) laboratory to simulate the PD faults of cylindrical voids in methacrylate, point-air-plane configuration, ceramic bushing with contaminated surface and a transformer affected by the internal PD. A PD dataset was collected, pre-processed and prepared for its use in the improved boosting algorithm using statistical techniques. In this paper, the EBA is extensively compared with the widely used single artificial neural network (SNN). Results show that the proposed approach can effectively improve the generalization capability of the PD patterns. The application of the proposed technique for both online and offline practical PD recognition is examined. View Full-Text
Keywords: condition monitoring; insulation diagnosis; electrical assets; partial discharge; artificial neural networks; single artificial neural network; ensemble-boosting algorithm condition monitoring; insulation diagnosis; electrical assets; partial discharge; artificial neural networks; single artificial neural network; ensemble-boosting algorithm
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Mas’ud, A.A.; Ardila-Rey, J.A.; Albarracín, R.; Muhammad-Sukki, F. An Ensemble-Boosting Algorithm for Classifying Partial Discharge Defects in Electrical Assets. Machines 2017, 5, 18.

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