DocumentCode
2825921
Title
SVM and Classification Ensembles based High-voltage Transmission Line Fault Diagnosis
Author
Bin, Shen ; Min, Yao ; Bo, Yuan
Author_Institution
Coll. of Comput., Zhejiang Univ., Hangzhou
fYear
2005
fDate
21-23 Sept. 2005
Firstpage
11
Lastpage
17
Abstract
This paper analyzes the inner mechanism of basic methods for high-voltage transmission line (HTL) fault diagnosis, and proposes the new SVM based HTL diagnosis models, which has the characteristic of good generalization. We also put forward the neural network ensembles model and multiple kinds of classifiers ensembles model based on the technology of classifier ensembles. These models can further promote the performance of single classifiers, such as traditional NN, rough set rules classifier, SVM etc. The simulation and experiments results completely show that our new models are more efficient than traditional ones
Keywords
fault diagnosis; neural nets; pattern classification; power engineering computing; power transmission faults; power transmission lines; rough set theory; support vector machines; SVM based HTL diagnosis models; classifier ensembles; high-voltage transmission line fault diagnosis; neural network ensembles model; rough set rules classifier; Fault diagnosis; Neural networks; Power system faults; Power system protection; Power system restoration; Power transmission lines; Support vector machine classification; Support vector machines; Transmission line theory; Transmission lines;
fLanguage
English
Publisher
ieee
Conference_Titel
Computer and Information Technology, 2005. CIT 2005. The Fifth International Conference on
Conference_Location
Shanghai
Print_ISBN
0-7695-2432-X
Type
conf
DOI
10.1109/CIT.2005.180
Filename
1562620
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