Modeling and Simulation of MPPT ZETA Converter Using Human Psychology Optimization Algorithm Under Partial Shading Condition

Abstract

The increasing development of renewable energy, especially photovoltaic (PV) more applicable in daily use. Unfortunately, PV has the disadvantage that it is vulnerable to shadow exposures that decrease power output depending on the scale of the shadow. The disadvantage includes the shadow of buildings, leaves, trees, etc. Shaded PV surface has two peak power conditions named Global Maximum Power Point (GMPP) and Local Maximum Power Point (LMPP). These conditions cause MPPT to be trapped in the LMPP so that the power obtained is not the actual power. The conventional method can be trapped in LMPP because it cannot distinguish GMPP and LMPP. These problems can be solved by using the Human Psychology Optimization (HPO) algorithm. This algorithm was chosen to overcome the effects of partial shading conditions so that MPPT can reach GMPP without getting stuck in LMPP. This algorithm is connected to ZETA Converter to produce real maximum power points. This research uses four shading patterns with different irradiation. HPO algorithm achieves the highest accuracy 99.99% with a tracking time 0.326 seconds occurring in the first shading pattern.