Enhanced modelling and control strategy for grid-connected PV system utilizing high-gain Quasi-Z source converter and optimized ANN-MPPT algorithm

Rising global energy demand and climate change urgency require a rapid shift to greener, sustainable energy sources. In response, this research aims to explore the challenges and opportunities inherent in this shift, focusing on the advancement and integration of renewable energy technology into existing systems. Accordingly, this research focuses on control design of solar photovoltaic (PV) grid-tied systems, incorporating a High Gain quasi Z-Source (HG-qZS) Converter. The primary objectives encompass a comprehensive exploration of solar PV system behaviour and development of an efficient grid-connected PV (GPV) solution. These goals are accomplished by a sophisticated maximum power point tracking (MPPT) controller that utilize modified artificial bee colony (MABC)-assisted artificial neural network (ANN) algorithm to enhance power extraction from solar PV modules. The resultant DC voltage produced by PV system is efficiently directed into \(1\Phi \;VSI\) $1\mathrm{\Phi }VSI$ for conversion into AC voltage. The resulting AC voltage is suitable for various applications within the electrical grid. Experimental verification using MATLAB demonstrates, notable advancement in solar PV technology, offering an environmentally friendly, highly efficient energy solution that aligns well with the increasing demand for clean and sustainable power generation.