PD classification by a modular neural network based on task decomposition

被引：18

作者：

Hong, T

Fang, MTC

Hilder, D

机构：

[1] Department of Electrical Engineering and Electronics, University of Liverpool, Liverpool

[2] Department of Automation, Tsinghua University

[3] Robinson Instruments Ltd., Runcorn, Cheshire

来源：

IEEE TRANSACTIONS ON DIELECTRICS AND ELECTRICAL INSULATION | 1996年 / 3卷 / 02期

关键词：

D O I：

10.1109/94.486772

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：

The use of modular neural network (MNN) for the recognition of partial discharge (PD) sources has been investigated. Three phase related quantities, the PD pulse counts, the average and maximum discharge magnitudes form the feature vector of a discharge signal. The MNN consists of 5 sub-networks with identical structure and a maximum selector. Each subnetwork is assigned the task to recognize a particular PD source. Compared with a single neural network which is trained to recognize all PD sources, the MNN has a higher training ability, faster rate of convergence and better recognition rate.

引用

页码：207 / 212

页数：6

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