The investigation of different pattern recognition performance as surge arrester fault classifier
Author:
Affiliation:
1. Faculty of Electrical and Computer Engineering, University of Science and Technology of Mazandaran, Behshahr, Iran
2. Faculty of Electrical and Robotic Engineering, Shahrood University of Technology, Shahrood, Iran
Publisher
IOS Press
Subject
Artificial Intelligence,General Engineering,Statistics and Probability
Reference37 articles.
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3. Doorsamy W. , Bokoro P. , On-line monitoring of metal-oxide surge arresters using improved equivalent model with evolutionary optimization algorithm, Industrial Electronics Conference (ISIE) (2017), 135–139.
4. High voltage ac resistive current measurements using a computer based digital watts technique;Coffeen;IEEE Transaction on Power Delivery,1991
5. Diagnostics and monitoring of metal-oxide surge arresters in high-voltage networks-comparison of existing and newly developed procedures;Heinrich;IEEE Transaction on Power Delivery,2001
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1. Modeling of Gapless MOSA at Low Current Region Considering Harmonic Content of Operating Voltage;Journal of The Institution of Engineers (India): Series B;2021-03-31
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