This article presents a study on the application of artificial neural networks (ANNs) for maximum power point tracking (MPPT) in photovoltaic (PV) systems using low-cost pyranometer sensors. The proposed approach integrates pyranometers, temperature sensors, and an ANN to estimate the duty cycle of a DC/DC converter, enabling the system to consistently operate at its maximum power point. The strategy was implemented in the local control of a Cuk converter and experimentally validated against the conventional Perturb and Observe (P&O) method. Results demonstrate that the ANN-based technique, leveraging affordable sensor technology, achieves accurate MPPT performance with reduced fluctuations, enhancing the responsiveness and efficiency of PV tracking systems.
@article{arxiv.2510.10313,
title = {Low-cost Pyranometer-Based ANN Approach for MPPT in Solar PV Systems},
author = {Luiz Fernando M. Arruda and Moises Ferber and Diego Greff},
journal= {arXiv preprint arXiv:2510.10313},
year = {2025}
}