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
Link To Document :
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