Referenced Items are not available for this document.
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
Published in:
Dielectrics and Electrical Insulation, IEEE Transactions on
(Volume:3
,
Issue:
2
)
Date of Publication: Apr 1996
- Page(s):
- 207 - 212
- ISSN :
- 1070-9878
- INSPEC Accession Number:
- 5269412
- Digital Object Identifier :
- 10.1109/94.486772
- Product Type:
- Journals & Magazines
- Date of Current Version :
- 06 August 2002
- Issue Date :
- Apr 1996
- Sponsored by :
- IEEE Dielectrics and Electrical Insulation Society
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