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Under partial shading conditions (PSCs), multiple local maximum power points (MPPs) may be exhibited on the P-U curve of photovoltaic systems. Direct control (DIRC) methods cannot extract the global MPP (GMPP); soft computing techniques can achieve it but are time consuming. This paper proposes a novel hybrid maximum power point tracking (MPPT) algorithm (INC-FA) combining incremental conductance (INC) and firefly algorithm (FA) to achieve better adaptability in various environments. INC is widely used because of its low-cost implementation and stability under rapidly changing atmospheric conditions, while FA is efficient in searching the GMPP. This combination (INC-FA) not only enables a faster global searching capability but also performs well as a DIRC method in the case of a single peak. In addition, INC-FA introduces the concept of the global optimal region and devises the population initialization mechanism to determine the initial position and population size of fireflies. Finally, the proposed algorithm is compared with three other MPPT methods under four different conditions. Simulation and experiment results demonstrate that the proposed algorithm can track the GMPP under various conditions with higher speed and accuracy.
Shi et al. (Wed,) studied this question.