scholarly journals Optimal proportional-integral controller design for statistic VAR compensator using particle swarm optimization algorithm

2019 ◽  
Vol 2 (8) ◽  
pp. 18-23
Author(s):  
E. Baëta ◽  
◽  
F. Ollennu ◽  
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Controller Design ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Proportional Integral ◽  
Integral Controller ◽  
Proportional Integral Controller

Optimal tuning of proportional–integral controller using particle swarm optimization algorithm for control of permanent magnet synchronous generator based wind turbine with tip speed ratio for maximum power point tracking

2020 ◽  
pp. 0309524X2090374
Author(s):  
Marwa Hannachi ◽  
Omessaad Elbeji ◽  
Mouna Benhamed ◽  
Lassaad Sbita
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Speed Ratio ◽  
Swarm Optimization ◽  
Proportional Integral ◽  
Point Tracking ◽  
Integral Controller ◽  
Proportional Integral Controller

This article deals with the study of the particle swarm optimization algorithm and its variants. After modeling the global system, a comparative study is carried out about the algorithms described in order to choose the best of those to be used thereafter. Then, the perturbed particle swarm optimization is presented to determine the optimal parameters of the proportional–integral controller for speed control to certify the tip speed ratio for maximum power point tracking of a wind energy conversion system. A numerical simulation is used in conjunction with the particle swarm optimization algorithm to determine the proportional–integral controller optimal parameters. From the simulations results, we observe that the proportional–integral controller designed with particle swarm optimization gives better results compared to the traditional method (proportional–integral manually) in terms of the performance index.

Optimal torque maximum power point technique for wind turbine: Proportional–integral controller tuning based on particle swarm optimization

2020 ◽  
pp. 0309524X1989290 ◽  
Author(s):  
Marwa Hannachi ◽  
Omessaad Elbeji ◽  
Mouna Benhamed ◽  
Lassaad Sbita
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Maximum Power Point ◽  
Swarm Optimization ◽  
Proportional Integral ◽  
Integral Controller ◽  
Proportional Integral Controller ◽  
Power Point

This article presents the problem of the energy system optimization for wind generators. The goal of this work is to maximize power extraction for a permanent magnet synchronous generator–based wind turbine with maximum power point technique. This goal is achieved using a proportional–integral controller for optimal torque tuning with the particle swarm optimization algorithm. In order to indicate the effectiveness and superiority of the particle swarm optimization algorithm–based proposal, a comparison with the genetic algorithm and the artificial bee colony algorithm is studied. The system is modeled and tested under MATLAB/Simulink environment. Simulation results validate the advantages of the designed particle swarm optimization–tuned proportional–integral controller compared to P&O and the proportional–integral controller manually in terms of performance index.

scholarly journals Sensorless Rotor-Field Oriented Controlled Induction Motor Drive with Particle Swarm Optimization Algorithm Speed Controller Design Strategy

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Yung-Chang Luo ◽  
Zhi-Sheng Ke ◽  
Ying-Piao Kuo
Keyword(s):  
Particle Swarm Optimization ◽  
Induction Motor ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Controller Design ◽  
Particle Swarm ◽  
Motor Drive ◽  
Induction Motor Drive ◽  
Swarm Optimization ◽  
Speed Controller

A sensorless rotor-field oriented control induction motor drive with particle swarm optimization algorithm speed controller design strategy is presented. First, the rotor-field oriented control scheme of induction motor is established. Then, the current-and-voltage serial-model rotor-flux estimator is developed to identify synchronous speed for coordinate transformation. Third, the rotor-shaft speed on-line estimation is established applying the model reference adaptive system method based on estimated rotor-flux. Fourth, the speed controller of sensorless induction motor drive is designed using particle swarm optimization algorithm. Simulation and experimental results confirm the effectiveness of the proposed approach.

scholarly journals Enhanced intelligent proportional-integral-like fuzzy knowledge–based controller using chaos-enhanced accelerated particle swarm optimization algorithm for transient calibration of air–fuel ratio control system

2019 ◽  
Vol 234 (1) ◽  
pp. 39-55
Author(s):  
Ziyang Li ◽  
Quan Zhou ◽  
Yunfan Zhang ◽  
Ji Li ◽  
Hongming Xu
Keyword(s):  
Particle Swarm Optimization ◽  
Objective Function ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Proportional Integral ◽  
Knowledge Based ◽  
Accelerated Particle Swarm Optimization ◽  
Fuzzy Knowledge

The self-adaptive and highly robust proportional-integral-like fuzzy knowledge–based controller has been developed to regulate air–fuel ratio for gasoline direct injection engines, in order to improve the transient response behaviour and reduce the effort to be spent on calibration of parameter settings. However, even though the proportional-integral-like fuzzy knowledge–based controller can automatically correct the initially calibrated proportional and integral parameters, a more appropriate selection of controller parameter settings will lead to better transient performance. Thus, this article proposes an enhanced intelligent proportional-integral-like fuzzy knowledge–based controller using chaos-enhanced accelerated particle swarm optimization algorithm to automatically define the most optimal parameter settings. An alternative time-domain objective function is applied for the transient calibration programme without the need for prior selection of the search-domain. The real-time transient performance of the enhanced controller is investigated on the air–fuel ratio control system of a gasoline direct injection engine. The experimental results show that the enhanced proportional-integral-like fuzzy knowledge–based controller based on chaos-enhanced accelerated particle swarm optimization is able to damp out the oscillations with less settling time (up to 75% reduction) and less integral of absolute error (up to 64.07% reduction) compared with the conventional self-adaptive proportional-integral-like fuzzy knowledge–based controller. Repeatability tests indicate that the chaos-enhanced accelerated particle swarm optimization algorithm–based proportional-integral-like fuzzy knowledge–based controller is also able to reduce the mean value of objective function by up to 10.61% reduction and the standard deviation of the objective function by up to 28.29% reduction, compared with the conventional accelerated particle swarm optimization algorithm–based proportional-integral-like fuzzy knowledge–based controller.

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