Grid-Connected Dual PV Management and Reliability Improvement based on Back Propagation Neural Network (BPNN-PSO)

Abstract

A single-phase grid-connected Photovoltaic (PV) system based on the Maximum Power Point Tracking Perturb and Observe Algorithm (P&O) technique MPPT. Due to interactions between different semiconductors and variable loads, the input source contains harmonic distortion, voltage sags and surges, and other power quality problems. As a solution, the Grid-connected Photovoltaic (P.V.) and neural network system for boosting electricity quality was proposed. The P&O-based MPPT technology addresses partial shadow issues and other imbalanced components that commonly affect PV arrays. The system consists of two PV panels connected in series, with each PV cell having identical attributes. This PV array's interaction with various irradiation patterns can be used to anticipate the PV array in question. The Back Propagation Neural Network (BPNN-PSO) technology has been proposed for lowering the Total Harmonic Distortion (THD) of PV array systems while increasing convergence and accuracy rates.