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Enhancing Solar Energy Efficiency Integrating Neural Networks with Maximum Power Point Tracking Systems

Author(s): Seham Ahmed Hashem1, Ali Abdulwahhab Abdulrazzaq2, and Raghad Hameed Ahmed3*

Published In : International Journal of Electrical and Electronics Research (IJEER)        Volume 13, Issue 1

Publisher : FOREX Publication

Published : 30 March 2025

e-ISSN : 2347-470X

Page(s) : 125-131

DOI: https://doi.org/10.37391/IJEER.130116

Abstract

This paper investigates the integration of artificial neural networks (ANN) into Maximum Power Point Tracking (MPPT) systems to enhance the efficiency and performance of solar photovoltaic (PV) systems. Conventional MPPT controllers, such as Perturb and Observe (P&O) and Incremental Conductance, often face challenges in maintaining optimal efficiency under rapidly changing environmental conditions. To address these challenges, this study proposes an innovative approach leveraging the adaptive learning capabilities of ANN. Extensive datasets, including solar irradiance, temperature, and power output under various conditions, were collected to design and train the ANN model. The ANN architecture features a multilayer structure with advanced activation functions, enabling effective handling of the nonlinear behavior of solar PV systems. The results demonstrated that ANN-based MPPT systems improved efficiency by up to 15% compared to traditional techniques, achieved a 25% increase in response speed, and exhibited greater stability, particularly under partial shading and rapid irradiance variations. This research not only enhances the efficiency of solar energy systems but also highlights the significant potential of machine learning in renewable energy technologies. Future studies could explore scaling this approach to accommodate diverse environmental conditions, paving the way for intelligent, AI-driven renewable energy solutions.

Keywords: Maximum Power Point Tracking (MPPT), Machine Learning in Energy Systems, Neural Networks, Perturb and Observe (P&O), Renewable Energy Technologies, Solar Photovoltaic Systems .




Seham Ahmed Hashem, Middle Technical University, Iraq; Email: dr.seham.ahmed@mtu.edu.iq

Ali Abdulwahhab Abdulrazzaq, Middle Technical University, Iraq; Email: dr.ali.abdulwahhab@mtu.edu.iq

Raghad Hameed Ahmed*, Middle Technical University, Iraq; Email: raghad.hammed@mtu.edu.iq

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Seham Ahmed Hashem, Ali Abdulwahhab Abdulrazzaq and Raghad Hameed Ahmed (2025), Enhancing Solar Energy Efficiency Integrating Neural Networks with Maximum Power Point Tracking Systems . IJEER 13(1), 125-131. DOI: 10.37391/IJEER.130116.