Comparative study of different types of DC/DC converters for PV systems using RBF neural network-based MPPT algorithm

Abstract

In this article, a detailed comparison of various DC/DC converter topologies for photovoltaic (PV) systems is presented, focusing on conventional step-up converters and advanced quadratic step-up converters. The research is part of an enhanced maximum power point tracking (MPPT) strategy, using a radial basis function (RBF) neural network. The RBF-based MPPT algorithm efficiently tracks the optimal duty cycle by accurately identifying the maximum power point (MPP) of the PV system under various irradiance and thermal conditions, ensuring maximum energy extraction. The system is modeled using MATLAB/Simulink. The simulations include a complete PV configuration comprising solar panels, a resistive load, and an MPPT controller regulated by Pulse Width Modulation (PWM) signals. Performance indicators such as energy conversion efficiency, response time, and system stability are evaluated for both inverter topologies. The results show the superiority of the quadratic boost converter in terms of higher voltage gains and improved efficiency under certain operating conditions while exposing its limitations in terms of complexity and cost. This analysis offers valuable insights for optimizing the choice of converters.

Keywords

• pv system
• conventional boost converter
• quadratic boost converter
• radial basis function neural network
• mppt