Estimation d'indicateurs de diagnostic pour la surveillance de panneaux photovoltaïques à l'aide de réseaux de neurones artificiels

ALRIFAI, Yehya; AGUILERA GONZALEZ, Adriana; VECHIU, Ionel

doi:10.1109/iccad60883.2024.10553687

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ALRIFAI, Yehya
ESTIA - Institute of technology [ESTIA]

AGUILERA GONZALEZ, Adriana

ESTIA - Institute of technology [ESTIA]

VECHIU, Ionel

ESTIA - Institute of technology [ESTIA]

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Communication dans un congrès

Ce document a été publié dans

2024 International Conference on Control, Automation and Diagnosis (ICCAD), 2024 International Conference on Control, Automation and Diagnosis (ICCAD), 2024-05-15, Paris. p. 1-6

IEEE

Résumé en anglais

Solar energy is widely recognized as one of the primary renewable energy sources. However, the efficiency and reliability of Photovoltaic (PV) systems can be significantly impacted by faults. For these reasons, it is paramount to Continuously monitor the PV health state to ensure optimal performance. In this context, this paper introduces a robust estimation model using an Artificial Neural Network (ANN) model to accurately predict three diagnosis indicators: power (P), current (I), and voltage (V). These indicators play a vital role in monitoring the behavior of PV systems considering different weather conditions. The computational algorithm establishes the mapping from PV electrical coordinates and temperature to the diagnosis indicators, without relying on an irradiation sensor. The performance of the proposed estimation model is evaluated via MATLAB/Simulink®, based on the real meteorological profiles for a typical year in Anglet, France.< Réduire

Mots clés en anglais

ANN

PV panels

Diagnosis indicators

Estimation

Temperature sensors

Photovoltaic systems

Radiation effects

Computational modeling

Artificial neural networks

Predictive models

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