Machine Learning-Based Fault Detection for Enhanced Reliability in Photovoltaic Systems

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