Performance Analysis of Squirrel-Cage Induction Motors Under Broken Rotor Bar and Stator Inter-Turn Fault Conditions Using Analytical Modeling

Srinivasan

Journal of Electrical and Computer Engineering

et al. 2019

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.

Section: Introductionmentioning

confidence: 99%

“…Motor current signature analysis (MCSA) is an important technique adopted for condition monitoring. Induction motor faults like bearing problems, broken rotor bar, eccentricity abnormalities, and stator winding faults cause variation in amplitude and frequency of motor current signature [3][4][5][6][7][8][9][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

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

Srinivasan

Journal of Electrical and Computer Engineering

et al. 2019

IEEJ Transactions Elec Engng

“…In addition to the MCSA method, scholars have also proposed some other fault diagnosis techniques for BRB faults based on other physical quantities, mainly including residual voltage method , instantaneous power method , electromagnetic torque method , analytical model method , zero‐sequence current spectrum method and so on. The residual voltage method is used to analyze the influence of BRB fault on the rotor loop‐current distribution, and the change law of the stator residual voltages after AC dump.…”

Section: Introductionmentioning

confidence: 99%

Online diagnosis of broken rotor bar fault of squirrel‐cage induction motor using a magnetic field measuring coil

2019

Broken rotor bar (BRB) fault may cause deterioration of motor performance and interfere with normal production. Accurate detection of BRB fault is helpful in arranging overhaul and replacing equipment in advance. According to the theory of magnetic potential balance, the variation rule of the air-gap flux density of the squirrel-cage induction motor (IM) before and after the BRB fault is deduced in this paper. A large-size measuring coil is embedded inside one stator slot wedge, and BRB fault is diagnosed by combining the time-domain and frequency-domain characteristics of the induced voltage of the measuring coil. In the stage of validity verification, a 200 kW and a 7.5 kW squirrel-cage IM are selected to complete the finite element simulation and fault-simulation experiments, respectively. Compared with the traditional motor current signal analysis method, the detection performance of BRB fault under different load levels is verified, which provides a new solution for the fault diagnosis of BRB fault of the squirrel-cage IM.

“…Thus the major problem is connected how to detect faults. Therefore, early detection of turns shortcircuits and broken bars during machine operation would remove following harm to adjacent coils and the stator and bearing at rotor [2,3]. In this context, during the last two decades, the fault diagnosis of in asynchronous machine has turn on interest great from researchers.…”

Section: Introductionmentioning

confidence: 99%

Induction Machine Faults Detection and Localization by Neural Networks Methods

Chouidira¹,

Khodja²,

Chakroune³

2019

RIA

The objective of this study is to present artificial intelligence (AI) technique for detection and localization of fault in induction machine fault, through a multi-winding model for the simulation of four adjacent broken bars and three-phase model for the simulation of shortcircuit between turns. In this work, it was found that the application of artificial neural networks (ANN) based on Root mean square values (RMS) plays a big role for fault detection and localization. The simulation and obtained results indicate that ANN is able to detect the faulty with high accuracy. Keywords:Induction machine, faults detection and localization, broken bars, artificial neural network (ANN), root mean square (RMS), multi winding, three-phase model = [V] − [R][I](1)