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

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 photovo...

Full description

Saved in:

Bibliographic Details
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
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Description
Summary:	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.
ISSN:	2156-3381 2156-3403 2156-3403 2156-3381
DOI:	10.1109/JPHOTOV.2024.3437736