An Unsupervised Gaussian Mixer Model for Detection and Localization of Partial Discharge Sources using RF Sensors

被引：19

作者：

Mishra, D. K. ^{[1
]}

Sarkar, B. ^{[1
]}

Koley, C. ^{[1
]}

Roy, N. K. ^{[1
]}

机构：

[1] Natl Inst Technol Durgapur, Elect Engn Dept, MG Rd, Burdwan 713209, W Bengal, India

来源：

IEEE TRANSACTIONS ON DIELECTRICS AND ELECTRICAL INSULATION | 2017年 / 24卷 / 04期

关键词：

Condition monitoring; Gaussian Mixer Model; partial discharge; RF sensor; source localization; time-delay-of-arrival; LOCATION; SYSTEM;

D O I：

10.1109/TDEI.2017.005976

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 presents an automated system for condition monitoring of power distribution utilities through detection and localization of partial discharge (PD) sources. Among the various outcomes of PDs, emission of Electromagnetic Magnetic wave mainly in the radio frequency (RF) and ultra-high-frequency (UHF) band, is an eminent phenomena. In the present work, a Gaussian Mixer Model (GMM) has been applied in an unsupervised manner, primarily for detecting presence of PD sources inside power system utilities, and thereafter for classification of PD events. Finally, time-delay-of-arrival (TDOA) based algorithm has been applied for localization of the PD sources. The proposed system has been tested in laboratory using different types of artificial PD sources and in different power distribution sub-stations. The obtained results were found to be reasonable encouraging.

引用

页码：2589 / 2598

页数：10

共 28 条

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[2]

Balanis C. A., 1997, Antenna Theory: Analysis and Design, V2nd

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