Solar photovoltaic (SPV) modules have a low output voltage and are load-dependent. Therefore, it is critical that the SPV system has an adequate DC–DC converter to regulate and improve the output voltage to get maximum output voltage. To meet load requirements, the voltage must be increased, necessitating the use of energy-efficient power electronic converters. The performance of an SPV system coupled to a high-gain quadratic boost converter (HG-QBC) with a load is investigated in this paper. The suggested HG-QBC for the SPV system at a lower value of duty ratio provides high voltage gain with a boost factor of four times. An analytical comparison is carried out with the various existing boost converters in terms of the components and the boost factor. The issue of locating the maximum power generation point from the SPV system is crucial. As a result, choosing an appropriate maximum power point tracker (MPPT)-based technique to obtain the peak power output of the SPV system under the rapidly varying atmospheric conditions is vital. To determine the highest output power of an SPV system, a hybrid-based MPPT with a neural network assisted by a perturb and observe (P&O) technique is proposed. For the HG-QBC, a comparison of the proposed MPPT with a traditional P&O-based MPPT is illustrated. The comparative analysis takes into account rise time, settling time and voltage ripples. The output voltage and power characteristics of the proposed model are analysed under constant and varying irradiation conditions using MATLAB®/Simulink®. The results of a hybrid-based MPPT show that the oscillations are minimum at the maximum power point with fewer ripples of 0.20% and a settling time of 1.2 s in comparison with the other two techniques.

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