Induction Motor Stator Interturn Short Circuit Fault Detection in Accordance with Line Current Sequence Components Using Artificial Neural Network
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 symmet...
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| Format: | Article |
| Language: | English |
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Wiley
2019-01-01
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| Series: | Journal of Electrical and Computer Engineering |
| Online Access: | http://dx.doi.org/10.1155/2019/4825787 |
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| author | Gayatridevi Rajamany Sekar Srinivasan Krishnan Rajamany Ramesh K. Natarajan |
| author_facet | Gayatridevi Rajamany Sekar Srinivasan Krishnan Rajamany Ramesh K. Natarajan |
| author_sort | Gayatridevi Rajamany |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-e82c5d9b249a4f39a2be429ffcc8d586 |
| institution | DOAJ |
| issn | 2090-0147 2090-0155 |
| language | English |
| publishDate | 2019-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Electrical and Computer Engineering |
| spelling | doaj-art-e82c5d9b249a4f39a2be429ffcc8d5862025-08-20T03:21:03ZengWileyJournal of Electrical and Computer Engineering2090-01472090-01552019-01-01201910.1155/2019/48257874825787Induction Motor Stator Interturn Short Circuit Fault Detection in Accordance with Line Current Sequence Components Using Artificial Neural NetworkGayatridevi Rajamany0Sekar Srinivasan1Krishnan Rajamany2Ramesh K. Natarajan3Dept. of EEE, KCG College of Technology, Chennai, IndiaDept. of EEE, Hindustan Institute of Technology and Science, Chennai, IndiaDept. of BCA, Krupanidhi Degree College, Bangalore, IndiaMechatronics and Motion Systems, Bonfiglioli Transmissions Private Limited, Bologna, ItalyThe 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.http://dx.doi.org/10.1155/2019/4825787 |
| spellingShingle | Gayatridevi Rajamany Sekar Srinivasan Krishnan Rajamany Ramesh K. Natarajan 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 |
| title | Induction Motor Stator Interturn Short Circuit Fault Detection in Accordance with Line Current Sequence Components Using Artificial Neural Network |
| title_full | Induction Motor Stator Interturn Short Circuit Fault Detection in Accordance with Line Current Sequence Components Using Artificial Neural Network |
| title_fullStr | Induction Motor Stator Interturn Short Circuit Fault Detection in Accordance with Line Current Sequence Components Using Artificial Neural Network |
| title_full_unstemmed | Induction Motor Stator Interturn Short Circuit Fault Detection in Accordance with Line Current Sequence Components Using Artificial Neural Network |
| title_short | Induction Motor Stator Interturn Short Circuit Fault Detection in Accordance with Line Current Sequence Components Using Artificial Neural Network |
| title_sort | induction motor stator interturn short circuit fault detection in accordance with line current sequence components using artificial neural network |
| url | http://dx.doi.org/10.1155/2019/4825787 |
| work_keys_str_mv | AT gayatridevirajamany inductionmotorstatorinterturnshortcircuitfaultdetectioninaccordancewithlinecurrentsequencecomponentsusingartificialneuralnetwork AT sekarsrinivasan inductionmotorstatorinterturnshortcircuitfaultdetectioninaccordancewithlinecurrentsequencecomponentsusingartificialneuralnetwork AT krishnanrajamany inductionmotorstatorinterturnshortcircuitfaultdetectioninaccordancewithlinecurrentsequencecomponentsusingartificialneuralnetwork AT rameshknatarajan inductionmotorstatorinterturnshortcircuitfaultdetectioninaccordancewithlinecurrentsequencecomponentsusingartificialneuralnetwork |