Variable step size perturb and observe MPPT controller by applying θ-modified krill herd algorithm-sliding mode controller under partially shaded conditions

Exhausting reservoirs of oils have led to investing in renewable energies to meet the ever-increasing energy demand in the world and mitigate the environmental impacts of conventional fuels. One of the prevalent renewable energy technologies is the solar photovoltaic (PV) system. These solar units face some challenges, mainly due to the volatile power generation and frequent oscillations, besides low rate when tracking the maximum power. Accordingly, a novel approach is suggested in this research work to address such drawbacks in the conventional control systems. The presented framework is based on a sliding mode controller (SMC) for the sake of maximum power point tracking (MPPT) applied to solar PV units, taking into account diverse meteorological conditions. In this respect, an efficient optimization algorithm, named “θ-modified krill herd (θ-MKH) method” has been proposed to optimally determine the parameters of the SMC. The operating conditions are online and in the grid-connected state. An overarching comparison has been made between the presented approach and other methods like hill climbing (HC), β technique, fractional Voc, incremental conductance (INC), fuzzy logic controller (FLC), and classic fixed-step perturb and observe (P&O) while taking into consideration diverse meteorological conditions. The developed control framework based on the SMC and θ-MKH guarantees the desirable operation of the PV system in terms of limited oscillations and stable operation with the efficiency of 99.5% in tacking the maximum power point in rational time.