Detection of Abnormal Conditions of Induction Motor by using ANN

Induction motors are the most common drive in today-s industries due of its salient features. But its operating condition may sometime led the machine into the different fault situations. The external faults like Over Load, single phasing, unbalanced supply Voltage, locked rotor, phase reversal, ground fault, Under Voltage & Over Voltage happens more rapidly. These faults can result in drastic consequences for an industrial process. Early detection of abnormalities in the motors will help to avoid expensive failures & also can lead to greater plant availability, extended life, higher quality product, and smoother plant operation. Extensive research has been conducted in the last decade & several Artificial Intelligence (AI) techniques like Artificial Neural Networks (ANNs), have been developed and applied in the monitoring processes of faults. ANN can be applied in induction motor relays which provide inexpensive but effective fault detection mechanism. This paper addresses the detection of an external motor faults (e. g. , unbalanced voltage, under voltage, over voltage) with a digital protection set by using an artificial neural network (ANN) for a three-phase induction motor of rating 3HP, 414 V, 6.4A, 50Hz Sq Cage IM 1480 rpm. The proposed set-up has been simulated using-Matlab/Simulink- Software and tested for various conditions of unbalance voltages. This dissertation also talks about the causes of unbalance voltage fault & its effect on performance of motor. Same is verified by MATLAB simulation results. The data sheet is created by using MATLAB results at various unbalance voltage conditions and it is used to train & test the neural network. Well trained neural network with most sensitive parameters are used to detect abnormal conditions with more accuracy. Data sheet can be also obtained by performing actual experimental setup on motor at normal & abnormal conditions. A Stator current signals can be captured by DSO. Processed & Sampled data can be used to train neural network for detecting abnormal condition. The simulated results clearly show that well-trained neural networks can precisely of early fault detection, also validating the proposed setup as a simple, reliable and effective protection for the three-phase induction motor.