Referenced Items are not available for this document.
This paper presents an automated online disturbance classification technique. This technique is based on wavelet multiresolution analysis and pattern recognition techniques. The wavelet-multiresolution transform is introduced as a powerful tool for feature extraction in order to classify different disturbances. Minimum Euclidean distance, k-nearest neighbor, and neural network classifiers are used to evaluate the efficiency of the extracted features.
Published in:
Power Delivery, IEEE Transactions on
(Volume:17
,
Issue:
3
)
Date of Publication: Jul 2002
- Page(s):
- 677 - 683
- ISSN :
- 0885-8977
- INSPEC Accession Number:
- 7363887
- Digital Object Identifier :
- 10.1109/TPWRD.2002.1022786
- Product Type:
- Journals & Magazines
- Date of Current Version :
- 07 November 2002
- Issue Date :
- Jul 2002
- Sponsored by :
- IEEE Power & Energy Society
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