scholarly journals Direct Torque Control of Induction Motor by Using Particle Swarm Optimization Technique

2019 ◽  
pp. 560-565
Author(s):  
Mohit Kumar Yadav ◽  
Somnath Sharma ◽  
Sumati Srivastava
Keyword(s):  
Particle Swarm Optimization ◽  
Induction Motor ◽  
Optimization Technique ◽  
Direct Torque Control ◽  
Particle Swarm ◽  
Torque Control ◽  
Swarm Optimization ◽  
Speed Controller ◽  
Particle Swarm Optimization Technique ◽  
Improved Performance

This paper is based on an efficient and reliable evolutionary approach of particle swarm optimization (PSO) using direct torque control (DTC) of induction motor. In order to resolve the problem of parameter variation the PI controllers are generally used in industrial plants because it is uncomplicated and robust. However, there is a problem in changing PI parameters. So, the engineers are looking for automatic tuning procedures. In traditional direct torque-controlled induction motor drive, there is generally undesired torque and ripple in form of flux. So Tuning PI parameters (Kp, Ki) are critical to DTC system to improve the performance of the system. In this paper, particle swarm optimization (PSO) is planned to correct the parameters (Kp, Ki) of the speed controller in order to get improved performance of the system and also responsible to run the machine at base speed.

A New Method of Speed Control for Induction Motor Based On Improved Particle Swarm Optimization

2016 ◽  
Vol 6 ◽  
pp. 25
Author(s):  
LingZhi Yi ◽  
Sui YongBo ◽  
Yu WenXin
Keyword(s):  
Particle Swarm Optimization ◽  
Induction Motor ◽  
Particle Swarm ◽  
Optimization Techniques ◽  
Swarm Optimization ◽  
Speed Controller ◽  
Inertia Coefficient ◽  
Improved Performance ◽  
Dynamic Inertia Weight ◽  
Dynamic Inertia

Optimization techniques are becoming more popular for the improvement in control of induction motor. Many intelligent algorithms have been used to improve performance of induction motor so for including particle swarm optimization. However, the improved performance may be limited on account of inertia coefficient in particle swarm optimization, which lead to the unbalance between the searching step and searching precision. In this paper, a variable-step nonlinear dynamic inertia weight of particle swarm optimization speed controller is proposed to improve the performance of an induction motor. The experiment results show that the proposed method has excellent performance.

scholarly journals Hybrid bacteria foraging-particle swarm optimization algorithm in DTC performance improving for induction motor drive

2021 ◽  
Vol 22 (2) ◽  
pp. 660
Author(s):  
Salah Eddine Rezgui ◽  
Hocine Benalla ◽  
Houda Bouhebel
Keyword(s):  
Particle Swarm Optimization ◽  
Induction Motor ◽  
Direct Torque Control ◽  
Particle Swarm ◽  
Optimization Method ◽  
Optimization Techniques ◽  
Torque Control ◽  
Swarm Optimization ◽  
Loop Control ◽  
Comparison Results

<p dir="ltr"><span>This paper presents a hybrid algorithm that combines the particle swarm optimization method with the bacteria foraging technique, named: BF-PSO. The aim is to achieve more efficient and precise parameters determination of the regulators that leads to performance improvement in the speed-loop control of an induction motor (IM) implemented in a direct torque control (DTC). The approach consists of tuning the proportional-integral (PI) parameters that meet high dynamics and tracking behavior using the hybrid BF-PSO algorithm. </span>Investigations have been completed with Matlab/Simulink and several performance tests are conducted. The comparison results are exposed with the most used indices in the controllers' tuning with optimization techniques. It will be shown that the presented technique presents better quality results compared to the conventional method of calculated PI.</p><div><span><br /></span></div>

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