Induction Motor Stator Interturn Short Circuit Fault Detection in Accordance with Line Current Sequence Components Using Artificial Neural Network
Joint Authors
Rajamany, Gayatridevi
Srinivasan, Sekar
Rajamany, Krishnan
Natarajan, Ramesh K.
Source
Journal of Electrical and Computer Engineering
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-12-11
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Information Technology and Computer Science
Abstract EN
The intention of fault detection is to detect the fault at the beginning stage and shut off the machine immediately to avoid motor failure due to the large fault current.
In this work, an online fault diagnosis of stator interturn fault of a three-phase induction motor based on the concept of symmetrical components is presented.
A mathematical model of an induction motor with turn fault is developed to interpret machine performance under fault.
A Simulink model of a three-phase induction motor with stator interturn fault is created for extraction of sequence components of current and voltage.
The negative sequence current can provide a decisive and rapid monitoring technique to detect stator interturn short circuit fault of the induction motor.
The per unit change in negative sequence current with positive sequence current is the main fault indicator which is imported to neural network architecture.
The output of the feedforward backpropagation neural network classifies the short circuit fault level of stator winding.
American Psychological Association (APA)
Rajamany, Gayatridevi& Srinivasan, Sekar& Rajamany, Krishnan& Natarajan, Ramesh K.. 2019. Induction Motor Stator Interturn Short Circuit Fault Detection in Accordance with Line Current Sequence Components Using Artificial Neural Network. Journal of Electrical and Computer Engineering،Vol. 2019, no. 2019, pp.1-11.
https://search.emarefa.net/detail/BIM-1173776
Modern Language Association (MLA)
Rajamany, Gayatridevi…[et al.]. Induction Motor Stator Interturn Short Circuit Fault Detection in Accordance with Line Current Sequence Components Using Artificial Neural Network. Journal of Electrical and Computer Engineering No. 2019 (2019), pp.1-11.
https://search.emarefa.net/detail/BIM-1173776
American Medical Association (AMA)
Rajamany, Gayatridevi& Srinivasan, Sekar& Rajamany, Krishnan& Natarajan, Ramesh K.. Induction Motor Stator Interturn Short Circuit Fault Detection in Accordance with Line Current Sequence Components Using Artificial Neural Network. Journal of Electrical and Computer Engineering. 2019. Vol. 2019, no. 2019, pp.1-11.
https://search.emarefa.net/detail/BIM-1173776
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1173776