Parameter estimation of three phase induction motor using gravitational search algorithm for IFOC

2015 ◽  
Author(s):  
T. Banerjee ◽  
G. Sarkar ◽  
J. Bera
Keyword(s):  
Parameter Estimation ◽  
Induction Motor ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Three Phase ◽  
Gravitational Search

Z-Source Inverter Fed Induction Motor Drive Control Using Hybrid Methodology

2017 ◽  
Vol 27 (03) ◽  
pp. 1850042 ◽  
Author(s):  
S. Mythili ◽  
K. Thiyagarajah
Keyword(s):  
Induction Motor ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Drive System ◽  
Voltage Source ◽  
Support Vector ◽  
Motor Drive ◽  
Hybrid Technique ◽  
Induction Motor Drive ◽  
Three Phase

In this paper, a hybrid technique is proposed for controlling the [Formula: see text]-source inverter fed induction motor drive system. The hybrid technique is the combination of the gravitational search algorithm (GSA) and the support vector machine (SVM), which is utilized to improve the performance of the induction motor (IM). The novelty of the study is to control the [Formula: see text]-Source Inverter for improving the stability and performance of the IM drive system with the help of the proposed hybrid technique. Subsequently, the total harmonic distortion (THD) is decreased and the oscillation period of the stator current, torque and speed are eliminated. The inputs of the proposed technique are motor speed and reference speed. The output of the proposed system is reference quadrature axis current. Moreover, the PI controller is optimized for getting an optimal result to produce reference quadrature axis current. After that, the SVM is used to predict the control pulses of voltage source inverter. Here, the three-phase reference current is used to generate the accurate control pulses. In three-phase reference current, SVM is trained by the input motor quadrature axis current and the reference quadrature axis current with the associated target reference. The proposed technique is implemented in the MATLAB/simulink platform. The performance of the proposed method is determined and compared with the existing methods such as PSO-SVM and SVM methods.

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