article · Algerian Journal of Signals and Systems
This research focuses on the modelling and control of a wind energy conversion system equipped with a permanent magnet synchronous generator. The central objective is to maximise the extraction of electrical power from the wind turbine. To achieve this, two distinct maximum power point tracking methods are examined and compared: particle swarm optimisation and a genetic algorithm. Both approaches were evaluated through simulations conducted in the Matlab and Simulink environment. The results show that deploying these meta-heuristic tracking algorithms allows the wind energy conversion system to reach the optimal mechanical speed required to achieve maximum power generation under operating conditions.
Wind turbines must continuously adapt to changing conditions to generate electricity efficiently. Applying optimisation algorithms to control turbine speed helps ensure that systems capture the greatest possible amount of clean energy from the wind, supporting more efficient renewable power generation.
The work could inform the development of control software for wind energy systems using permanent magnet synchronous generators, aimed at turbine manufacturers and wind farm operators. Because the evaluation is limited strictly to Matlab and Simulink simulations, the technology remains at an early, theoretical stage of research and requires practical validation on physical hardware before commercial deployment.
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The modeling and control of a wind energy conversion system (WECS) that makes use of a permanent magnet synchronous generator (PMSG) are investigated in this work. It compares two methods—MPPT-PSO and MPPT-GA—with the aim of maximizing power extraction from the system in each case. The simulation results demonstrate that the use of these MPPT algorithms enables the system to reach the optimal mechanical speed necessary for maximum power output. All simulations were performed using Matlab/Simulink.
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DOI: 10.51485/ajss.v10i1.256
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