Hybrid Optimization Technique-Based Maximum Power Point Tracking for Single-Stage Grid-Connected PV Systems

In this study, a hybrid strategy is proposed for maximum-power-point-tracking (MPPT) of a single-stage grid-connected solar photovoltaic system. The proposed approach combines the performance of Garra-Rufa fish optimization (GRFO) and student psychology optimization-algorithm (SPOA). The GRFO approach is updated using the SPOA approach; hence, it is named as GRFO-SPOA approach. The key objective of proposed work is to improve global maximum point tracking under all situations together with partial shading. PV source, current controller, voltage controller, DC-to-AC converter, and grid are all parts of the proposed system. The proposed technique tunes the optimal duty cycle of DC-to-AC converter. The proposed method has several benefits: it simplifies the tracking structure, decreases computation time, and replaces sophisticated maximum power point tracking (MPPT) control with a simpler structure while keeping optimal results. Additionally, cleaning frequency determination for impure photovoltaic modules is developed in terms of relationship among velocity of dust deposition, density of dust deposition and power efficiency of photovoltaic module. The proposed hybrid technique is executed in MATLAB at dissimilar cases, and the performance is executed to existing approaches. The result of comparison shows that the proposed hybrid system is efficiently employed to obtain maximal power as solar photovoltaic module and improving power quality of PV power generation.