Cover
Vol. 21 No. 2 (2025)

Published: December 16, 2025

Pages: 237-250

Original Article

Improvement of Extracted Photovoltaic Power Using Artificial Neural Networks MPPT with Enhanced Flyback Controller

Afrah Abdul Kadhim 1 Abdulhasan Abdulhasan Fatimah Jaber
1Basrah Engineering Technical College, Southern Technical University, Iraq
History
  • Received: March 8, 2024
  • Revised: July 5, 2024
  • Accepted: July 19, 2024
  • Available Online: February 28, 2025
DOI

https://doi.org/10.37917/ijeee.21.2.22

Abstract

Due to the nonlinear electrical properties of PV generators, the width and performance of these frames could be enhanced by 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 proposed system. The hybrid Flyback/ANN controller is based on teaching and training a neural network, where the dataset is utilized to adjust the levitation converter which is taken care of by a stand-alone photovoltaic generator (PVG) with a Flyback controller. It is assumed that the results will be obtained by the ANN-MPPT system with the Flyback controller which provides low motions and shows a great implementation around the maximum power point compared to the PVG used with traditional MPPT algorithms such as Perturbation and Observation (P & O).

Keywords

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