A novel methodology for simulating maximum power point trackers using mine blast optimization and teaching learning based optimization algorithms for partially shaded photovoltaic system

When the photovoltaic (PV) system is fully illuminated, the voltage-power characteristic of the array has one maximum power point (MPP). This point can be tracked by conventional maximum power point tracker algorithms. On the other hand, in partially shaded PV, the voltage - power characteristic has only one global MPP (GMPP) and multiple local MPPs. It is important to operate the PV system at GMPP for achieving optimal operation. This paper presents the application of two novel meta-heuristic optimization algorithms to extract GMPP from the PV system under partial shading conditions: The mine blast algorithm (MBA) and the teaching learning based optimization algorithm (TLBO). A proposed constrained objective function representing the PV array output power is also presented. Different patterns of shadows that strike the PV array surface are studied. The studied patterns are uniform ones, changing from 0% to 375% with steps of 25%, and non-uniform patterns with different locations of GMPP. The obtained results from each algorithm are compared, and the results show that the MBA based tracker is more reliable, more efficient, and superior to TLBO. A comparison with fuzzy logic control and adaptive neuro-fuzzy and particle swarm optimization based trackers has been done. The results ensure that the reliability of the MBA in solving the problem is addressed.