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Transformer differential protection using wavelet transform
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AI
DOI：10.1016/j.epsr.2014.04.008.png)
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Abstract
En 中文
This paper will propose a cascade of minimum description length criterion with entropy approach along with artificial neural network (ANN) as an optimal feature extraction and selection tool for a wavelet packet transform based transformer differential protection. The proposed protection method provides a reliable and computationally efficient tool for distinguishing between internal faults and inrush currents. The role of minimum description length criterion with entropy approach has been found to improve the efficiency of ANN with the dimensionality reduction of the feature vector. This reduction plays a major role in preventing the redundancy effect that can occur when using several features in an intelligent based monitoring system. (C) 2014 Elsevier B.V. All rights reserved.
Keywords:
Entropy approach
Minimum description length criterion
Inrush current
Internal fault
Power transformer
Wavelet packet analysis
Journal
IF:
4.2
Papers:
1.1W
Citations:
2.2W