Data-Driven Digital Inspection of Photovoltaic Panels Using a Portable Hybrid Model Combining Meteorological Data and Image Processing

• Published in: IEEE journal of photovoltaics Vol. 14; no. 6; pp. 937 - 950
• Main Authors: Oufadel, Ayoub, Azouzoute, Alae, Ghennioui, Hicham, Soubai, Chaimae, Taabane, Ibrahim
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01-11-2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Artificial neural networks; Convolutional neural network (CNN); Convolutional neural networks; Data analysis; Data collection; Data models; Digital imaging; Engineering Sciences; Fault detection; Image classification; Image enhancement; Image processing; innovative inspection; Inspection; Machine learning; machine learning (ML); maintenance; Meteorological data; Meteorology; Panels; photovoltaic; Portable equipment; Solar energy; Solar panels; Support vector machines; Temperature distribution; Temperature measurement; image processing; Temperature distribution; photovoltaic; Inspection; machine learning (ML); Convolutional neural network (CNN); Solar panels; Temperature measurement; Support vector machines; innovative inspection; Accuracy; Data models; maintenance
• DOI: 10.1109/JPHOTOV.2024.3437736
• EISSN: 2156-3403; 2156-3381
• ISSN: 2156-3381; 2156-3403

This article proposes a novel approach to photovoltaic panel inspection through the integration of image classification and meteorological data analysis. Utilizing two convolutional neural network models with distinct architectures for classifying thermal and red, green, blue (RGB) images of photovoltaic installations, in addition to an support vector machines model for meteorological data classification, the results from these models are concatenated, allowing the fusion of visual and meteorological information for comprehensive defect detection. Data collection from photovoltaic panels is achieved using a portable device, followed by the application of advanced image processing techniques to identify faults rapidly and accurately with up to 96% accuracy. The inspection results are presented in a user-friendly format, facilitating straightforward interpretation and analysis. This new approach has the potential to significantly enhance the efficiency and durability of solar energy systems, enabling timely maintenance and repair for photovoltaic panel issues.