Improvement of Extracted Photovoltaic Power Using Artificial Neural Networks MPPT with Enhanced Flyback Controller - Abdul Kadhim, Afrah; Abdulhasan, Abdulhasan; Jaber, Fatimah (Iraqi Journal for Electrical and Electronic Engineering, 2025)
Due to the nonlinear electrical properties of PV generators, the width and performance of these frames could be enhancedby carrying them to operate at ultimate energy mark tracking. In this study, a versatile maximum power point tracking(MPPT) model using a modified Flyback controller with artificial neural network (ANN) technique as our proposedsystem. The hybrid Flyback/ANN controller is based on teaching and training a neural network, where the dataset isutilized to adjust the levitation converter which is taken care of by a stand-alone photovoltaic generator (PVG) with aFlyback controller. It is assumed that the results will be obtained by the ANN-MPPT system with the Flyback controllerwhich provides low motions and shows a great implementation around the maximum power point compared to the PVGused with traditional MPPT algorithms such as Perturbation and Observation (P & O).