A Novel Hybrid Arithmetic-Based Grey Wolf Optimization Method for Tracking the Global Maximum Power Point of Photovoltaic Systems Under Unequal Irradiance Patterns

Solar power generation (SPG) is uncertain and less efficient under unequal shading conditions. It has a highly nonlinear power versus voltage (P–V) curve that has multiple peaks which are categorized as a global maximum power point (GMPP) and local maximum power point (LMPP). It requires a better tracking method that continuously tracks the GMPP instead of the LMPP. Many conventional methods are unable to track the GMPP. The meta-heuristic methods are the better solutions for this problem. However, still there is a need to improve the performance in terms of tracking speed, tracking efficiency and better exploration and exploitation phases, which are the essential parameters in intelligent optimization. This paper proposed a new hybrid meta-heuristic technique, arithmetic-based grey wolf optimization (AGWO), a combination of arithmetic optimization algorithm (AOA) and grey wolf optimization (GWO). The first one is mainly for the exploration phase, and the other is for the exploitation phase; together, the combination enhances the search behaviour of the algorithm. The proposed AGWO was executed in MATLAB/Simulink and evaluated with a real-time simulator to validate its performance. This is compared with other relevant methods like AOA, GWO, particle swarm optimization (PSO) and perturb and observe (P&O) methods. The results indicated that the suggested AGWO technique shows better improvement in tracking efficiency of about 16.41% more than P&O during pattern 1. Also, it shows an excellent tracking speed as compared with PSO is 60.42% raise during pattern 3.