Recognition of partial discharges using an ensemble of neural networks

A.A. Mas' ud; B.G. Stewart; S.G. McMeekin; A. Nesbitt

doi:10.1109/CEIDP.2011.6232703

Recognition of partial discharges using an ensemble of neural networks

A.A. Mas' ud, B.G. Stewart, S.G. McMeekin, A. Nesbitt

SCEBE School Office

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

This paper introduces an improved method for classifying Partial Discharge (PD) patterns using Ensemble Neural Network (ENN) learning. The method is based on training several Neural Network (NN) models and combining their predictions. In this paper it is applied to the recognition of PD from artificially created poly-ethylene-terephthalate (PET) voids and in particular the ability of the ENN to categorise statistical F-q-n patterns for two different void sizes over 50 and 250 power cycles. The training data for the ENN comprises statistical parameters obtained from voids of 0.6mm and 1mm diameter.

Original language	English
Title of host publication	Proceedings of the 2011 IEEE Conference on Electrical Insulation and Dielectric Phenomena (CEIDP 2011)
Publisher	IEEE
Pages	497-500
Number of pages	4
ISBN (Print)	9781457709852
DOIs	https://doi.org/10.1109/CEIDP.2011.6232703
Publication status	Published - 2011

Keywords

partial discharge
neural networks
pattern classification

Documents and Links

10.1109/CEIDP.2011.6232703

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Mas' ud, A. A., Stewart, B. G., McMeekin, S. G., & Nesbitt, A. (2011). Recognition of partial discharges using an ensemble of neural networks. In Proceedings of the 2011 IEEE Conference on Electrical Insulation and Dielectric Phenomena (CEIDP 2011) (pp. 497-500). IEEE. https://doi.org/10.1109/CEIDP.2011.6232703

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abstract = "This paper introduces an improved method for classifying Partial Discharge (PD) patterns using Ensemble Neural Network (ENN) learning. The method is based on training several Neural Network (NN) models and combining their predictions. In this paper it is applied to the recognition of PD from artificially created poly-ethylene-terephthalate (PET) voids and in particular the ability of the ENN to categorise statistical F-q-n patterns for two different void sizes over 50 and 250 power cycles. The training data for the ENN comprises statistical parameters obtained from voids of 0.6mm and 1mm diameter.",

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N2 - This paper introduces an improved method for classifying Partial Discharge (PD) patterns using Ensemble Neural Network (ENN) learning. The method is based on training several Neural Network (NN) models and combining their predictions. In this paper it is applied to the recognition of PD from artificially created poly-ethylene-terephthalate (PET) voids and in particular the ability of the ENN to categorise statistical F-q-n patterns for two different void sizes over 50 and 250 power cycles. The training data for the ENN comprises statistical parameters obtained from voids of 0.6mm and 1mm diameter.

AB - This paper introduces an improved method for classifying Partial Discharge (PD) patterns using Ensemble Neural Network (ENN) learning. The method is based on training several Neural Network (NN) models and combining their predictions. In this paper it is applied to the recognition of PD from artificially created poly-ethylene-terephthalate (PET) voids and in particular the ability of the ENN to categorise statistical F-q-n patterns for two different void sizes over 50 and 250 power cycles. The training data for the ENN comprises statistical parameters obtained from voids of 0.6mm and 1mm diameter.

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KW - neural networks

KW - pattern classification

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DO - 10.1109/CEIDP.2011.6232703

M3 - Conference contribution

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BT - Proceedings of the 2011 IEEE Conference on Electrical Insulation and Dielectric Phenomena (CEIDP 2011)

PB - IEEE

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