Neural Network Based Classification of Partial Discharge in HV Motors

被引：0

作者：

Asiri, Yahya ^{[1
]}

Vouk, Alfred ^{[1
]}

Renforth, Lee ^{[2
]}

Clark, David ^{[3
]}

Copper, Jack ^{[4
]}

机构：

[1] Saudi Aramco, Elect Syst Div, Dhahran, Saudi Arabia

[2] HVPD Ltd, Manchester, Lancs, England

[3] Univ Manchester, Manchester M13 9PL, Lancs, England

[4] NeuralWare, Pittsburgh, PA USA

来源：

2011 ELECTRICAL INSULATION CONFERENCE (EIC) | 2011年

关键词：

Partial Discharge; Neural Networks; Pre-processing; Multiple Defects;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

This paper discusses the general application of using Neural Networks (NN) to classify six different types of Partial Discharge (PD). Stator winding failures contribute about 30-40% of the total motor failures according to IEEE and EPRI. Ninety percent (90%) of electrical failures on High-Voltage (HV) equipment are related to insulation deterioration. Large datasets were collected for motors with PD defects as well as PD-free machines. The datasets of PD were pre-processed and prepared for use with a NN using statistical means. It was possible to utilise the advantages offered by multiple NN models to classify the PD defects with a maximum recognition rate of 94.5% achieved, whereas previous research work did not exceed a classification accuracy of 79%.

引用

页码：333 / 339

页数：7

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