Genetic Optimization of a PD Diagnostic System for Cable Accessories

被引：30

作者：

Rizzi, Antonello ^{[1
]}

Mascioli, Fabio Massimo Frattale ^{[1
]}

Baldini, Francesco ^{[1
]}

Mazzetti, Carlo ^{[1
]}

Bartnikas, R. ^{[1
]}

机构：

[1] Univ Roma La Sapienza, I-00184 Rome, Italy

来源：

IEEE TRANSACTIONS ON POWER DELIVERY | 2009年 / 24卷 / 03期

关键词：

Automatic feature selection; cable accessories; fuzzy neural networks; genetic algorithms (GAs); partial-discharge (PD) patterns classification; XLPE; PATTERN-RECOGNITION; NEURAL-NETWORKS; PULSE SHAPES; FUZZY; CLASSIFICATION;

D O I：

10.1109/TPWRD.2009.2016826

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：

An automatic procedure, based on a genetic algorithm capable of optimizing a diagnostic system for the recognition and identification of partial-discharge (PD) pulse patterns in the terminations and joints of solid dielectric extruded power distribution cables, is described. The core of the diagnostic system is a fuzzy neural network, namely a Min-Max classifier. The genetic optimization is capable for reducing the system complexity, while enhancing its diagnostic performance. The developed procedure is sufficiently general to be applied to PD source identification in the cables themselves as well as other electric power apparatus.

引用

页码：1728 / 1738

页数：11

共 26 条

[1] Partial discharge pattern classification using the fuzzy decision tree approach [J].