A dynamic particles MPPT method for photovoltaic systems under partial shading conditions

Abstract Combining with cuckoo search (CS) algorithm, this paper proposes a novel dynamic particles maximum power point tracking (MPPT) method to track global maximum power point (GMPP) for photovoltaic (PV) systems under partial shading conditions (PSCs). Based on the investigation of the transient characteristic of the boost converter, the concept of dynamic sample time is introduced and deployed to decrease system tracking time with a fuzzy positioning mechanism. In the proposed two-mode searching strategy, numbers of particles, which are gradually abandoned through several iterations in global mode, rapidly position the area near the GMPP. Then the local mode is activated to accurately track the GMPP. These implements allow traversal search of numbers of initial particles in overall area to handle random shading patterns of PV arrays with fewer iterations and less sample time compared to conventional optimization algorithm based MPPT techniques. Thus, the success rate of tracking GMPP is maximized with shorter tracking time and lower tracking power loss under PSCs. Comprehensive simulations and experiments are performed to verify the better effectiveness of the proposed dynamic particles MPPT method in comparison to other existing advanced optimization algorithms based MPPT techniques in PV systems under PSCs.

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