Abstract
Photovoltaic energy generation systems are characterized by the dependence of their output power on environmental conditions and load matching. The power conversion stage serves as an intermediate adaptation interface that matches the operating point of the source to an optimal operating point (Maximum Power Point MPP). In this paper, different versions of computational intelligence based Maximum Power Point Tracking (MPPT) algorithms illustrated using the Particle Swarm Optimization algorithm are implemented in a DSP-based experimental setup to evaluate their performance for tracking the MPP in various operating conditions. Experimental results show the enhanced power conversion efficiency obtained by using computational intelligence techniques in photovoltaic energy generation systems.
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