Fault Classification in Photovoltaic Power Plants Using Machine Learning

José Leandro da Silva, Dionicio Zocimo Ñaupari Huatuco and Yuri Percy Molina Rodriguez ()
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José Leandro da Silva: Department of Electrical Engineering, Federal University of Paraíba, João Pessoa 58052-520, PB, Brazil
Dionicio Zocimo Ñaupari Huatuco: Faculty of Electrical and Electronic Engineering, National University of Engineering, Lima 15333, Peru
Yuri Percy Molina Rodriguez: Department of Electrical Engineering, Federal University of Paraíba, João Pessoa 58052-520, PB, Brazil

Energies, 2025, vol. 18, issue 17, 1-21

Abstract: The growing deployment of photovoltaic (PV) power plants has made reliable fault detection and classification a critical challenge for ensuring operational efficiency, safety, and economic viability. Faults on the direct current (DC) side, especially during the commissioning phase, can significantly affect power output and maintenance costs. This paper proposes a fault classification methodology for the direct current (DC) side of PV power plants, using the MATLAB/Simulink 2023b simulation environment for system modeling and dataset generation. The method accounts for different environmental and operational conditions—including irradiance and temperature variations—to enhance fault identification robustness. The main electrical faults—such as open circuit (OC), short circuit (SC), connector faults, and partial shading—are analyzed based on features extracted from current–voltage (I–V) and power–voltage (P–V) curves. The proposed classification system achieved 100% accuracy by applying the One-Versus-One (OVO) and One-Versus-Rest (OVR) techniques, using a dataset with 704 samples for one string and 2480 samples for three strings. The lowest accuracies were observed with the OVO technique: 99.03% for 1024 samples with one string, and 97.35% for 880 samples with three strings. The study also highlights the performance of multiclass machine learning techniques across different dataset sizes. The results reinforce the relevance of using machine learning integrated into the commissioning phase of PV systems, with the potential to improve reliability, reduce losses, and optimize the operational costs of solar plants. Future work should explore the application of this method to real-world data, as well as its deployment in the field to support companies and professionals in the sector.

Keywords: fault classification; photovoltaic systems; feature extraction from I–V and P–V curves; machine learning detection; Operation and Maintenance (O&M) (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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