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arXiv · 2510.10313

Low-cost Pyranometer-Based ANN Approach for MPPT in Solar PV Systems

Luiz Fernando M. Arruda·Moises Ferber·Diego Greff
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Abstract

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.

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BibTeXRIS

Luiz Fernando M. Arruda, Moises Ferber, Diego Greff. 2025-10-11. Low-cost Pyranometer-Based ANN Approach for MPPT in Solar PV Systems. https://arxiv.org/abs/2510.10313

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