Optimization by Genetic Algorithm of a Wind Energy System applied to a Dual-feed Generator
Received: 13 June 2024 | Revised: 12 July 2024 | Accepted: 16 July 2024 | Online: 24 August 2024
Corresponding author: Mourad Guediri
Abstract
In an attempt to improve wind energy production using a Doubly Fed Induction Generator (DFIG), this paper presents a model for power maximization through controlling the turbine speed by utilizing a Maximum Peak Power Tracking (MPPT) controller, and through also controlling the stator active and reactive power for DFIG. In the context of increasing the search for new electric energy production sources, including renewable energies, Proportional Integral (PI) contributed to the modeling of the control and improvement of the wind energy conversion system, with the aim of exploiting wind energy to produce clean energy without pollution. To enhance the benefits of classic PI regulators, and so obtain efficient performance, the study seeks to determine the parameters of PI regulators. PI is used for wind turbines without including classical analytical methods for final calculation. Thus, optimization algorithms, namely Genetic Algorithms (GA) or Particle Swarm Optimization (PSO), which seek to minimize the error in a controlled system between the input signal and the output signal, were developed in this study. The basis of this approach is the management of both reactive and active power. In order to increase performance and efficiency, the new approach incorporates GA ideas into the control technology used in the wind turbine. The simulation results derived after this incorporation provide wind turbine systems that are more stable and efficient producing significantly better results than traditional PI regulators. Then, a simulation program, which includes the artificial intelligence controls and GA, is created in Matlab.
Keywords:
Doubly Fed Induction Generator (DFIG), Maximum Peak Power Tracking (MPPT), Proportional Integral (PI), maximizaton of wind power production, Hybrid Genetic Algorithm (HGA)Downloads
References
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Copyright (c) 2024 Mourad Guediri, Nabil Ikhlef, Hocine Bouchekhou, Abdelhafid Guediri, Abdelkarim Guediri
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