New Hybrid MPPT Technique Using Honey Badger (HBA) and Coyote Optimizer (COA) for Partially Shaded PV systems

Abstract

The maximum production of electricity is needed to satisfy the consumption from renewable energy source such as solar energy. Solar cell is more appropriate energy source for long usage. The irradiance level is measured for finding the maximum earned power from solar energy so that several maximum power point tracking (MPPT) techniques have been introduced. Thus the performance of photovoltaic (PV) system is increased by using Honey Badger Algorithm (HBA) and Coyote Optimizer Algorithm (COA). The HBA is extracted from the foraging behavior of honey badger which is used to get maximum power from the PV panel. Additionally COA optimization is used for making efficient and accurate output power by analyzing the placement of PV panel in the whole process based on the social behavior of coyotes. From these algorithms, the maximum power is obtained in any climate change situations and also the efficiency and accuracy are maintained using the proposed optimization techniques. The novelty of this study is to produce maximum power and also maintain the efficiency and accuracy of output throughout the process. The simulation is carried out under Matlab/Simulink environment. The key results analyzed the performance of the proposed technique, in terms of steady state behavior, tracking speed, tracking efficiency, and distortions in waveforms and was compared to the most recent and effective MPPT algorithms as FPA, GSA and PSO, under different solar radiance levels, and the comparison showed that is superior to them, a faster tracking speed, higher tracking efficiency, and lower oscillations around the steady state.