Comparative analysis of PSO, MFO, GOA AND ABC algorithms and hybrid approach proposal for increasing mgnt performance in photovoltaic systems

With the advancement of technology, the increasing energy demand and the environmental damage caused by fossil fuels are increasing the importance of renewable energy sources. Among these sources, solar energy stands out due to its low installation cost and ease of maintenance. Photovoltaic (PV) systems used in solar power plants have the potential to generate electricity without harming the environment. However, environmental factors such as irradiance, temperature, and atmospheric variables significantly affect the efficiency of PV systems. Therefore, maximum power point tracking (MPPT) algorithms that optimize production by determining the maximum power point (MPPT) need to be developed. Traditional fixed-step MPPT methods fail to perform adequately under variable environmental conditions and lead to energy losses. Therefore, optimization algorithms inspired by nature are widely used in MPPT processes today. These algorithms were developed by taking inspiration from the behavior, environmental adaptations, and problem-solving abilities of living organisms. In this study, the MGNT performances of four different nature-inspired optimization algorithms (Moth Flame Optimization (MFO), Grasshopper Optimization (GOA), Artificial Bee Colony (ABC), and the traditional Particle Swarm Optimization (PSO) method were comparatively investigated in the MATLAB/SIMULINK environment. In addition, the use of GOA and ABC algorithms as a hybrid was also evaluated. Simulations reveal the performance of the algorithms in terms of tracking speed, accuracy, stability, and energy efficiency under different irradiance and temperature conditions. The results show that the nature-inspired algorithms provide successful and stable results, especially under partial shading and rapid environmental change conditions.