An adaptive neural network for the estimation of torque and air gap flux signals in the direct field oriented control of an induction motor

1995 ◽  
Author(s):  
W.P. Hew
Keyword(s):  
Neural Network ◽  
Induction Motor ◽  
Air Gap ◽  
Field Oriented Control ◽  
Adaptive Neural Network

scholarly journals STATIC AIR-GAP ECCENTRICITY FAULT DETECTION OF INDUCTION MOTOR USING ARTIFICIAL NEURAL NETWORK (ANN)

2020 ◽  
Vol 8 (8) ◽  
pp. 377-385
Author(s):  
KHALED MOHAMMED BIR GAMAL ◽  
SUPRIYA P. PANDA ◽  
M. V. RAMANA MURTHY
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fault Detection ◽  
Induction Motor ◽  
Back Propagation ◽  
Air Gap ◽  
Operating Conditions ◽  
Detection Techniques ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Induction motor plays an important role in the industrial, commercial and residential industries, owing to its immense advantages over the opposite types of motors. Such motors have to operate under different operating conditions that cause engine degradation leading to fault occurrences. There are numerous fault detection techniques available. There are numerous fault detection techniques available. The technique used in this paper to prove the effect of static air gap eccentricity on behaving or performing of the three-phase induction motor is the artificial neural network (ANN) as ANN depends on detecting the fault on the amplitude of positive and negative harmonics of frequencies. In this paper, we used two motors to achieve real malfunctions and to get the required data and for three different load tests. In this paper, we adopted MCSA to detect the fault based on the stator current. The ANN training algorithm used in this paper is back propagation and feed forward. The inputs of ANN are the speed and the amplitudes of the positive and the negative harmonics, and the type of fault is the output. To distinguish between healthy and faulty motor, the input data of ANN are well-trained via experiments test. The methodology applied in this paper was MATLAB and present how we can distinguish between healthy and faulty motor.

scholarly journals CARRIER BASED PWM TECHNIQUE AND ADAPTIVE NEURAL NETWORK BASED ROTOR RESISTANCE ESTIMATOR FOR THE PERFORMANCE ENHANCEMENT OF VECTOR CONTROLLED INDUCTION MOTOR DRIVES

2019 ◽  
Vol 16 (1) ◽  
pp. 63 ◽  
Author(s):  
Venkadesan A
Keyword(s):  
Neural Network ◽  
Induction Motor ◽  
Pulse Width Modulation ◽  
Performance Enhancement ◽  
Motor Drive ◽  
Induction Motor Drive ◽  
Adaptive Neural Network ◽  
Rotor Resistance ◽  
Set Up ◽  
Vector Modulation

In this paper, the carrier based Pulse Width Modulation (PWM) technique and neural network based rotor resistance estimator are proposed for vector controller Induction motor (IM) drives. The popular sine PWM is used for induction motor drive. The popular sine PWM has poor harmonic profile and (DC) utilization. The space vector modulation (SVM) technique overcomes the disadvantages of sine PWM. But SVM is computationally complex. Hence a simple PWM technique namely “carrier based PWM technique” similar to SVM is identified and proposed for vector controlled IM drive. The experimental set up is built up and the performance of carrier based PWM is validated using FPGA processor. The adaptive neural network based rotor resistance estimator in predictive mode is proposed for the vector controlled induction motor drive. The performance enhancement of the drive with carrier based PWM and rotor resistance estimator is comprehensively presented. 

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