Modified honey badger algorithm based global MPPT for triple-junction solar photovoltaic system under partial shading condition and global optimization

In the recent era, Metaheuristic Algorithms (MHs) are developed to tackle many and different optimization problems. The merit of the MHs is not only its simplicity to understand but also its easiness of implementation which helps in tackling different and various real-world applications. Honey Badger Algorithm (HBA) is one of the recent MHs. Although the success of the HBA algorithm in solving complex problems with high dimensions, it suffers from several drawbacks such as; 1) being trapped in local optima issue, 2) low convergence, and 3) the imbalance between exploration and exploitation stages. Therefore, an efficient local search called Dimensional Learning Hunting (DLH) is injected to the HBA to tackle these drawbacks, the proposed method named mHBA. On the other side, the demand of the PV systems (PVSs)’ installations is rapidly increasing from a day to the next. Due to the intermittency of the weather, the PV array performs as a nonlinear component in terms of its output characteristics. Partial shading is an environmental phenomenon that prevents all or part of the light to be exposed to the PV cells. This phenomenon makes the cell's output power curve fluctuates and hence contains multiple peaks and has an essential impact on the entire output of the PV system. However, identifying the peak that has the global maximum output power is the main target of most of the conventional maximum power point tracking (MPPT) methods. In this context, the higher efficiency of the global MPPT technique becomes a must to operate the PV cells as close as possible to the global MPPT. Accordingly, the performance of the proposed mHBA is assessed based on the complex 2020 IEEE Congress on Evolutionary Computation (CEC′20) test suite. Then, it is applied to track the global MPPT of PV system-based triple-junction solar cells (TJSCs) under partial shading conditions, four shading scenarios have been considered. The results of the proposed mHBA are compared with common MHs including Gray wolf optimizer (GWO), Moth-flame optimizer (MFO), Whale optimization algorithm, Sine cosine algorithm (SCA), Salp swarm algorithm (SSA), Tunicate swarm algorithm (TSA), Sooty Tern Optimization Algorithm (STOA), and the original HBA. The findings of this research proved the effectiveness and robustness of the proposed mBHA for solving the global optimization problems of the MPPT as an engineering application.