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.

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.

scholarly journals Maximum Power Point Tracking Method for Photovoltaic System Based on Enhanced Particle Swarm Optimization Algorithm Under Partial Shading Condition

2020 ◽  
Vol 13 (6) ◽  
pp. 241-254
Author(s):  
Anas Kamil ◽  
◽  
Mahmoud Nasr ◽  
Shamam Alwash ◽  
◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Maximum Power ◽  
Global Maximum ◽  
Maximum Power Point ◽  
Partial Shading ◽  
Swarm Optimization ◽  
Power Point

The maximum power point tracking (MPPT) is an essential key to ensure that the photovoltaic (PV) system is operated at the highest possible power generation. This paper presents an efficient MPPT method for the PV system based on an enhanced particle swarm optimization algorithm to track the location of the global maximum power point, whatever its location changes in the search space under all environmental conditions, including the partial shading on strings. In this paper, the formulation of the conventional particle swarm optimization algorithm is enhanced to decrease the searching time and the oscillation of the generated output power as well as the power losses in the online tracking process. This enhancement can be achieved by utilizing a special time-varying weighting coefficient and removing the effect of some other coefficients in the conventional particle swarm optimization algorithm (PSO) that cause winding of the particles during the online tracking process. Test results verified the accuracy of the proposed method to track the global maximum power point with considering the effect of partial shading condition. The proposed method was also compared with other MPPT methods to verify the superiority of the proposed work. The obtained results reveal that the proposed method is effective to improve the tracking efficiency and reduce the tracking time and the number of iterations for the different irradiances and load conditions. The maximum number of iterations was 11 iteration and the highest tracking time was 0.273s with tracking efficiency of about 99.98%.

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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