Salvato in:
| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2510.10313 |
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Sommario:
- 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.