Signal Processing Techniques for On-Line Partial Discharge Detection and Classification

被引：0

作者：

Aranburu, Alex ^{[1
]}

Olaizola, Jon ^{[1
]}

Barrenechea, Maitane ^{[1
]}

Mulroy, Patrick ^{[2
]}

Hurtado, Aritz ^{[2
]}

Gilbert, Ian ^{[2
]}

机构：

[1] Mondragon Unibertsitatea, Arrasate Mondragon, Gipuzkoa, Spain

[2] AIE, Ormazabal Corp Technol, Amorebieta, Bizkaia, Spain

来源：

2016 24TH EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO) | 2016年

关键词：

partial discharge; PD; pattern recognition; classification; extension set theory; PATTERN-RECOGNITION; NEURAL-NETWORK;

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：

Partial discharge (PD) detection plays a fundamental role in monitoring the health of medium voltage (MV) systems. This paper presents a method for PD detection and source recognition in MV sub-stations based on a combination of signal processing techniques. Firstly, PD detection and signal conditioning is carried out. Then, PDs of different sources are separated and finally classified by means of the extension set theory. The obtained results show a classification effectiveness of 100% on single source PDs and an effectiveness of 72.5% in multisource PDs, where PDs from many sources are captured in the same data set.

引用

页码：1433 / 1437

页数：5

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