scholarly journals DESIGN OF HIGH EFFICIENCY 4 KW INDUCTION MOTOR

Author(s):  
Bekir Dursun ◽  
Yunus Uzun
2020 ◽  
Vol 1 (1) ◽  
pp. 1-6
Author(s):  
P.P.S Saputra

Currently induction motors are widely used in industry due to strong construction, high efficiency, and cheap maintenance. Machine maintenance is needed to prolong the life of the induction motor. As studied, bearing faults may account for 42% -50% of all motor failures. In general it is due to manufacturing faults, lack of lubrication, and installation errors. Misalignment of motor is one of the installation errors. This paper is concerned to simulation of discrete wavelet transform for identifying misalignment in induction motor. Modelling of motor operation is introduced in this paper as normal operation and two variations of misalignment. For this task, haar and coiflet discrete wavelet transform in first level until fifth level is used to extract vibration signal of motor into high frequency of signal. Then, energy signal and other signal extraction gotten from high frequency signal is evaluated to analysis condition of motor. The results show that haar discrete wavelet transform at thirth level can identify normal motor  and misalignment motor conditions well


2021 ◽  
Vol 11 (6) ◽  
pp. 7861-7866
Author(s):  
N. H. Mugheri ◽  
M. U. Keerio ◽  
S. Chandio ◽  
R. H. Memon

The Three Phase Induction Motor (TIM) is one of the most widely used motors due to its low price, robustness, low maintenance cost, and high efficiency. In this paper, a Support Vector Regression (SVR) based controller for TIM speed control using Indirect Vector Control (IVC) is presented. The IVC method is more frequently used because it enables better speed control of the TIM with higher dynamic performance. Artificial Neural Network (ANN) controllers have been widely used for TIM speed control for several reasons such as their ability to successfully train without prior knowledge of the mathematical model, their learning ability, and their fast implementation speed. The SVR-based controller overcomes the drawbacks of the ANN-based controller, i.e. its low accuracy, overfitting, and poor generalization ability. The speed response under the proposed controller is faster in terms of rising and settling time. The dynamic speed response of the proposed controller is also superior to that of the ANN-PI controller. The performance of the proposed controller was compared for TIM speed control with an ANN-PI controller via simulations in SIMULINK.


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