Abstract:
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. By learning patterns associated with various types of short
circuit faults, the ANN model can distinguish between normal variations and true
fault occurrences. The results demonstrate that the ANN-based method achieves
high accuracy in identifying faults under diverse conditions, outperforming
traditional fault detection approaches in terms of response time and fault
classification precision. This approach not only enhances the fault detection
capabilities of PV systems but also minimizes maintenance costs and downtime
by enabling predictive maintenance. The study underscores the potential of ANN
models in advancing fault detection methodologies for PV systems, supporting
their efficient and reliable operation in sustainable energy infrastructures.
