AI-based visual analysis of photovoltaic panels for fault detection and maintenance support
Scientific Reports, cilt.16, sa.1, 2026 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 16 Sayı: 1
- Basım Tarihi: 2026
- Doi Numarası: 10.1038/s41598-026-51711-8
- Dergi Adı: Scientific Reports
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, BIOSIS, Chemical Abstracts Core, EMBASE, MEDLINE, Directory of Open Access Journals, Zoological Record, Academic Search Ultimate (EBSCO), Natural Science Collection (ProQuest), Biological Science Database (ProQuest), Biomedical Reference Collection: Corporate Edition (EBSCO), Health Research Premium Collection (ProQuest)
- Anahtar Kelimeler: Cross-domain evaluation, Deep feature extraction, EfficientNet, Multi-dataset validation, Photovoltaic defect classification, Transfer learning
- Ankara Yıldırım Beyazıt Üniversitesi Adresli: Evet
Özet
As the global transition toward renewable energy accelerates, ensuring the operational reliability of photovoltaic (PV) systems has become increasingly critical. Manual inspection procedures remain labor-intensive and economically inefficient, particularly in large-scale solar installations. This study proposes an automated diagnostic framework for classifying anomalies associated with PV panels using deep learning approaches built on the EfficientNet as a backbone, techniques such as Fine-tuned Transfer Learning (FTL), Deep Feature Extraction + Classifier (DFE-C), and Alternative Fine-tuning Setup (AFS) are applied and their efficacy in detecting various defect categories is evaluated. Furthermore, the analysis focuses on a comprehensive evaluation of these strategies in terms of robustness and accuracy regarding classification capabilities. The approaches are evaluated under a consistent data partitioning strategy derived from the same PV image dataset, enabling a systematic comparison of their classification accuracy, robustness, and consistency. The results indicate that FTL enhances domain adaptability, while DFE-C exhibits the greatest overall stability and performance under limited and variable data conditions. The AFS approach provides a balanced trade-off between flexibility and convergence. The experimental framework incorporates structured training pipelines, hyperparameter control, and performance benchmarking using accuracy, macro F1-score, and fold-based stability analysis. Specifically, the DFE-C approach achieved a superior overall accuracy of 94.05%, demonstrating near-perfect diagnostic capability in critical categories such as physical damage (100%) and snow coverage (96%). In short, the proposed framework provides a comparative evaluation methodology for automated PV inspection and offers useful insights for the development of more reliable AI-based diagnostic systems for PV energy applications.