The benefits of allowing for increased starting current in AC squirrel cage induction motors

1991 ◽  
Vol 27 (6) ◽  
pp. 1169-1174 ◽  
Author(s):  
A.H. Bonnett
Keyword(s):  
Induction Motors ◽  
Starting Current ◽  
Squirrel Cage

scholarly journals Intelligent Starting Current-Based Fault Identification of an Induction Motor Operating under Various Power Quality Issues

Energies ◽  
2021 ◽  
Vol 14 (2) ◽  
pp. 304
Author(s):  
Sakthivel Ganesan ◽  
Prince Winston David ◽  
Praveen Kumar Balachandran ◽  
Devakirubakaran Samithas
Keyword(s):  
Standard Deviation ◽  
Power Quality ◽  
Monitoring System ◽  
Condition Monitoring ◽  
Classification Accuracy ◽  
Induction Motors ◽  
Quality Data ◽  
Starting Current ◽  
Quality Issues ◽  
Condition Monitoring System

Since most of our industries use induction motors, it is essential to develop condition monitoring systems. Nowadays, industries have power quality issues such as sag, swell, harmonics, and transients. Thus, a condition monitoring system should have the ability to detect various faults, even in the presence of power quality issues. Most of the fault diagnosis and condition monitoring methods proposed earlier misidentified the faults and caused the condition monitoring system to fail because of misclassification due to power quality. The proposed method uses power quality data along with starting current data to identify the broken rotor bar and bearing fault in induction motors. The discrete wavelet transform (DWT) is used to decompose the current waveform, and then different features such as mean, standard deviation, entropy, and norm are calculated. The neural network (NN) classifier is used for classifying the faults and for analyzing the classification accuracy for various cases. The classification accuracy is 96.7% while considering power quality issues, whereas in a typical case, it is 93.3%. The proposed methodology is suitable for hardware implementation, which merges mean, standard deviation, entropy, and norm with the consideration of power quality issues, and the trained NN proves stable in the detection of the rotor and bearing faults.

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