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>

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.

scholarly journals Induction Motor Fault Classification Based on FCBF-PSO Feature Selection Method

2020 ◽  
Vol 10 (15) ◽  
pp. 5383 ◽  
Author(s):  
Chun-Yao Lee ◽  
Wen-Cheng Lin
Keyword(s):  
Feature Selection ◽  
Particle Swarm Optimization ◽  
Induction Motor ◽  
Particle Swarm ◽  
Feature Selection Method ◽  
Optimization Method ◽  
Typical Feature ◽  
Fault Classification ◽  
Selection Methods ◽  
Swarm Optimization

This study proposes a fast correlation-based filter with particle-swarm optimization method. In FCBF–PSO, the weights of the features selected by the fast correlation-based filter are optimized and combined with backpropagation neural network as a classifier to identify the faults of induction motors. Three significant parts were applied to support the FCBF–PSO. First, Hilbert–Huang transforms were used to analyze the current signals of motor normal, bearing damage, broken rotor bars and short circuits in stator windings. Second, ReliefF, symmetrical uncertainty and FCBF three feature-selection methods were applied to select the important features after the feature was captured. Moreover, the accuracy comparison was performed. Third, particle-swarm optimization (PSO) was combined to optimize the selected feature weights which were used to obtain the best solution. The results showed excellent performance of the FCBF–PSO for the induction motor fault classification such as had fewer feature numbers and better identification ability. In addition, the analyzed of the induction motor fault in this study was applied with the different operating environments, namely, SNR = 40 dB, SNR = 30 dB and SNR = 20 dB. The FCBF–PSO proposed by this research could also get the higher accuracy than typical feature-selection methods of ReliefF, SU and FCBF.

CHARACTERIZATION OF RECYCLED RUBBER USING PARTICLE SWARM OPTIMIZATION TECHNIQUES

2015 ◽  
Vol 88 (3) ◽  
pp. 343-358 ◽  
Author(s):  
I. Uriarte ◽  
E. Zulueta ◽  
T. Guraya ◽  
M. Arsuaga ◽  
I. Garitaonandia ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Viscoelastic Model ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Maxwell Model ◽  
Optimization Method ◽  
Optimization Techniques ◽  
Compression Tests ◽  
Swarm Optimization ◽  
Recycled Rubber

ABSTRACT A material based on recycled rubber has been developed to use as a protective coating on road barriers with the aim of improving motorcyclists' security against crash impacts. This material is based on grounded rubber from used tires added by extrusion using low-density polyethylene as adhesive. Compression tests have been performed for different densities of the recycled material to fully describe the mechanical characteristics under high strain rates (in the rank 0.057–5.7 s−1), and a constitutive model composed of a hyperelastic Mooney Rivlin part and a viscoelastic part based on the generalized Maxwell model has been taken to characterize this behavior. Hyperelastic parameters have been obtained by means of the least-squares fitting technique, and particle swarm optimization (PSO) has been used to obtain viscoelastic parameters. The PSO algorithm is shown to be a good optimization method, simple, versatile, and consisting of few parameters that accelerate to the optimal solution. Therefore, this article presents a new and efficient approach to obtaining the parameters for the viscoelastic model. The behavior of the experimental material confirms the theoretically obtained results, so the procedure presented in the article is validated successfully.

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 A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

2015 ◽  
Vol 2015 ◽  
pp. 1-38 ◽  
Author(s):  
Yudong Zhang ◽  
Shuihua Wang ◽  
Genlin Ji
Keyword(s):  
Particle Swarm Optimization ◽  
Ant Colony Algorithm ◽  
Communication Theory ◽  
Parallel Implementation ◽  
Particle Swarm ◽  
Optimization Method ◽  
Optimization Techniques ◽  
Swarm Optimization ◽  
Von Neumann ◽  
Automation Control

Particle swarm optimization (PSO) is a heuristic global optimization method, proposed originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used optimization techniques. This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO), population topology (as fully connected, von Neumann, ring, star, random, etc.), hybridization (with genetic algorithm, simulated annealing, Tabu search, artificial immune system, ant colony algorithm, artificial bee colony, differential evolution, harmonic search, and biogeography-based optimization), extensions (to multiobjective, constrained, discrete, and binary optimization), theoretical analysis (parameter selection and tuning, and convergence analysis), and parallel implementation (in multicore, multiprocessor, GPU, and cloud computing forms). On the other hand, we offered a survey on applications of PSO to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology. It is hoped that this survey would be beneficial for the researchers studying PSO algorithms.

scholarly journals Comprehensive learning particle swarm optimization for sizing and placement of distributed generation for network loss reduction

2020 ◽  
Vol 20 (1) ◽  
pp. 16 ◽  
Author(s):  
Eshan Karunarathne ◽  
Jagadeesh Pasupuleti ◽  
Janaka Ekanayake ◽  
Dilini Almeida
Keyword(s):  
Particle Swarm Optimization ◽  
Distributed Generation ◽  
Optimization Technique ◽  
Particle Swarm ◽  
Distribution Networks ◽  
Optimization Techniques ◽  
Premature Convergence ◽  
Optimal Placement ◽  
Swarm Optimization ◽  
Comparison Results

With the technological advancements, Distributed Generation (DG) has become a common method of overwhelming the issues like power losses and voltage drops which accompanies with the leaf of the feeders of radial distribution networks. Many researchers have used several optimization techniques and tools which could be used to locate and size the DG units in the system. Particle Swarm Optimization (PSO) is one of the famous optimization techniques. However, the premature convergence is identified as a fundamental adverse effect of this optimization technique. Therefore, the optimization problem can direct the objective function to a local minimum. This paper presents a variant of PSO techniques, “Comprehensive Learning Particle Swarm Optimization (CLPSO)” to determine the optimal placement and sizing of the DGs, which uses a novel learning strategy whereby all other particles’ historical best information and learning probability value are used to update a particle’s velocity. The CLPSO particles learn from one exampler for few iterations, instead of learing from global and personal best values in every iteration in PSO and this technique retains the swarm's variability to avoid premature convergence. A detailed analysis was conducted for the IEEE 33 bus system. The comparison results have revealed a higher convergence and an accuracy than the PSO.

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