Fault diagnosis system with machine learning and cloud processing for a photovoltaic solar system1

Abstract

ABSTRACT Photovoltaic solar energy plays an important role in renewable energy, but its performance can be affected by operational issues such as partial shading and soiling accumulation on the modules. In this context, intelligent monitoring strategies are essential for identifying faults and assessing performance. This study aimed to develop and implement a cloud-enabled intelligent fault inference system for a photovoltaic installation, using locally acquired electrical and environmental data, preprocessed prior to cloud-based inference. The model was trained using machine learning and artificial neural network techniques with real experimental data collected at the photovoltaic plant of the Laboratory of Physics and Renewable Energy at the University of Pernambuco, Petrolina Campus. The neural network predictions were integrated into a web-based platform for data visualization, parameter analysis, and automated operational alerts. The system showed high precision in identifying the evaluated conditions, with Mean Square Error on the order of 10⁻2, Mean Absolute Error between 5.33 and 5.40%, and coefficients of determination (R2) ranging from 99.74 to 99.76%. These results indicate the potential of the proposed approach to support monitoring and predictive maintenance of photovoltaic systems.