Résumé:
This paper presents a novel approach for fault detection in
photovoltaic (PV) systems using an Artificial Neural Network (ANN) model,
designed to improve operational reliability and enhance power generation
efficiency. As PV systems are increasingly integrated into the power grid, early
and accurate fault detection becomes critical to maintain consistent energy
production and avoid system damage. Traditional fault detection methods, such
as threshold-based monitoring technique, often struggles with adaptability and
precision due to variations in environmental conditions and system
configurations. In this work, we propose an ANN-based fault detection method
that addresses these challenges by leveraging the ANN’s ability to learn and
adapt to complex, nonlinear relationships between system parameters.
The proposed model is trained using historical data from PV systems, including
current, voltage, temperature, and irradiance, under both normal and faulty
operating conditions
