Global Maximum Power Point Tracking-Based Computational Intelligence Techniques

The photovoltaic (PV) systems are gaining popularity for both stand-alone and grid-connected applications. These systems offer benefits of being static, modular, environmental friendly, and converts light from the sun, which is a perennial source of clean and green energy. The energy conversion in the PV system is although instantaneous, yet less efficient because of optical and electrical losses. The optical loss caused by partial shading reduces PV system output greatly, if not properly mitigated. The reduced efficiency of shaded PV arrays is a significant obstacle in the rapid growth of solar power systems. The shading mitigation techniques ensure global peak operation of PV plant under undesirable shading condition. Multitudes of such mitigation techniques are available in the literature, though each one of them exhibits some vulnerability. The maximum power point tracking-based computational intelligence techniques that properly detect the global MPP are stated and discussed. Artificial neural networks (ANN), fuzzy logic control (FLC), and different types of meta-heuristic algorithm have been used such as particle swarm optimization (PSO), ant colony optimization (ACO), artificial bee colony optimization (ABC), genetic algorithm optimization (GA), differential evolution optimization (DE), cuckoo optimization (CS), firefly optimization (FA), grey wolf optimization (GWO), and bat optimization (BA). This chapter presents the proposed approaches in each method and provides a brief discussion of their characteristics. This comprehensive review of shading mitigation techniques would certainly help the researcher to select appropriate mitigation techniques for a given PV application.