Journal
ELECTRIC POWER SYSTEMS RESEARCH
Volume 143, Issue -, Pages 347-356Publisher
ELSEVIER SCIENCE SA
DOI: 10.1016/j.epsr.2016.09.031
Keywords
Three-phase induction motor; Artificial neural networks; Information measures; Stator fault
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Funding
- Federal University of Technology - Parana
- Coordination for the Improvement of Higher Level Personnel (CAPES)
- Araucaria Foundation [338/2012, 06/56093-3]
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The three-phase induction motors are considered one of the most important elements of the industrial process. However, in this environment, these machines are subject to electrical and mechanical faults, which may cause significant financial losses. Thus, the purpose of this paper is to present a pattern recognition method for the detection of stator windings short circuits based on measures of mutual information between the phase current signals. In order to validate the proposed patterns, feature vectors obtained from normal and faulty motors are applied to two topologies of artificial neural networks. The classification results presented accuracies over 93% even when the motors were subject to several conditions of load torque and power supply voltage unbalance. (C) 2016 Elsevier B.V. All rights reserved.
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