Induction Motor Inter-turn Short Circuit Fault Detection Using Efficient Feature Extraction for Machine Learning Based Fault Detectors

2019 ◽  
pp. 362-378
Author(s):  
Muhammad Mubashir Hussain ◽  
Tariq Jadoon ◽  
Mian M. Awais
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Fault Detection ◽  
Induction Motor ◽  
Short Circuit ◽  
Short Circuit Fault ◽  
Fault Detectors

scholarly journals Induction Motor Stator Interturn Short Circuit Fault Detection in Accordance with Line Current Sequence Components Using Artificial Neural Network

2019 ◽  
Vol 2019 ◽  
pp. 1-11
Author(s):  
Gayatridevi Rajamany ◽  
Sekar Srinivasan ◽  
Krishnan Rajamany ◽  
Ramesh K. Natarajan
Keyword(s):  
Neural Network ◽  
Fault Detection ◽  
Induction Motor ◽  
Short Circuit ◽  
Positive Sequence ◽  
Negative Sequence ◽  
Three Phase ◽  
Sequence Components ◽  
Negative Sequence Current ◽  
Short Circuit Fault

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.

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