A comprehensive evaluation of multicategory classification methods for fault classification in series compensated transmission line

This paper presents a new method for fault classification in series-compensated transmission line using multiclass support vector machine (MCSVM) and multi class extreme learning machine (MCELM). These methods use the information obtained from the wavelet decomposition of fault current signals for f...

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Bibliographic Details
Published in:Neural computing & applications 2010-06, Vol.19 (4), p.595-600
Main Authors: Malathi, V., Marimuthu, N. S., Baskar, S.
Format: Article
Language:English
Subjects:
Applied sciences
Artificial Intelligence
Computational Biology/Bioinformatics
Computational Science and Engineering
Computer Science
Computer science
control theory
systems
Data Mining and Knowledge Discovery
Exact sciences and technology
Fourier analysis
Image Processing and Computer Vision
Learning and adaptive systems
Mathematical analysis
Mathematics
Multivariate analysis
Numerical analysis
Numerical analysis. Scientific computation
Original Article
Probability and statistics
Probability and Statistics in Computer Science
Sciences and techniques of general use
Statistics
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Summary:This paper presents a new method for fault classification in series-compensated transmission line using multiclass support vector machine (MCSVM) and multi class extreme learning machine (MCELM). These methods use the information obtained from the wavelet decomposition of fault current signals for fault classification. Using MATLAB simulink, data set has been generated with different types of fault and system variables, which include the fault resistance, fault distance, load angle and fault inception angle. The proposed method has been tested on a 400-kV, 300-km transmission line under variety of fault conditions. The performance of MCSVM and MCELM is compared in terms of training time and classification accuracy. The comparisons have been made for both One-Versus-One and One-Versus-Rest methods of SVMs and ELMs. Results show that MCELMs need less training time compared to MCSVMs, and the classification accuracy of MCELMs is more or less similar to MCSVMs. The feasibility of the proposed methods is also tested on a practical 220-kV series-compensated transmission line, and the results obtained are quite promising.
ISSN:0941-0643
1433-3058
DOI:10.1007/s00521-009-0312-9