Abstract
In recent years, much research has been done to improve the performance of a wind energy conversion system at various wind speeds using Maximum Power Point Tracking (MPPT) algorithms to find an efficient production of peak energy.In most cases, the MPPT techniques used to maximize the turbines power coefficient neglect the effect of losses generated by the system components, which can shift the true optimal operating point of the wind turbine. Furthermore, conventional MPPT methods such as Perturbation and Observation (P&O) and Incremental of Conductance (IncCond), need to sense both the rotor speed and the power of the wind turbine. In addition, other methods such as Fuzzy Logic and neural networks based on MPPT algorithms are proposed but these methods require knowledge of the wind speed and system parameters for the training phase.In this context, a new MPPT technique is being proposed in this paper, based on Particle Swarm Optimization (PSO) for a standalone Self-Excited Induction Generator (SEIG) operating at variable wind speed and supplying an induction motor coupled to a centrifugal pump.
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