Publications Scientifiques

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    Wind Turbine Mechanical Speed Regulation Reliability Of Artificial Intelligent PSO-FLC Control
    (Institute of Electrical and Electronics Engineers Inc, 2024) Arabi, Marwa; Zennir, Youcef; Bourourou, Fares
    this paper addresses modeling and control of a wind energy conversion system (WECS). The WECS based on Permanent Magnet Synchronous Generator, PMSG. Wind energy transformed to mechanical energy via blade and turbine to give speed and torque to the PMSG. This mechanical speed will be controlled firstly with a classical MPPT-PI then will be optimized by a PSO algorithm, after that a new intelligent controller MPPT-FLC will be applied to show the efficiency of that's kind of controller on our WECS mechanical speed control. The analysis and discussion of the simulation results aim to enhance the reliability and efficiency of each suggested approach. Keywords - Wind turbine, Speed control, Optimization, FLC, PSO, PI.
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    Dynamic Performance Improvement of DFIM based on Hybrid Computational Technique
    (IEEE, 2021) Zidani, Mohamed Yazid; Brakta, Noureddine; Bendjeghaba, Omar
    This paper presents a hybrid intelligent nonlinear control, based on particle swarm optimization (PSO) technique and artificial intelligence controller (AI) to improve the dynamic performance of the system. These controllers are destined for the speed control of Doubly Fed Induction Motor (DFIM). The proportional-integral controller for speed regulation of the induction motor is the most extensively used controller. However, given the various operating conditions and the nature of parameter variability, the PI controller has some drawbacks. So, one of the frequently discussed applications of artificial intelligence (AI) in control is the replacement of a proportional integral speed controller with Artificial Neural Network (ANN) speed controller but the choice of the gain’s parameters controller is one of the main problems. So, Particle Swarm Optimization (PSO) technique on optimization performance is added to the PI and ANN controllers to find the best gain values. The simulation results for different scenarios illustrate the high performance of the proposed artificial intelligence controller for DFIM running at variable speeds in terms of consistency and stability