Open Access

ARTICLE

Feature Extraction and Classification of Photovoltaic Panels Based on Convolutional Neural Network

S. Prabhakaran^1,*, R. Annie Uthra¹, J. Preetharoselyn²

1 Department of Computational Intelligence, SRM Institute of Science and Technology, Chengalpattu, 603203, India
2 Department of Electrical Engineering, SRM Institute of Science and Technology, Chengalpattu, 603203, India

* Corresponding Author: S. Prabhakaran. Email:

Computers, Materials & Continua 2023, 74(1), 1437-1455. https://doi.org/10.32604/cmc.2023.032300

Received 13 May 2022; Accepted 15 June 2022; Issue published 22 September 2022

Abstract

Photovoltaic (PV) boards are a perfect way to create eco-friendly power from daylight. The defects in the PV panels are caused by various conditions; such defective PV panels need continuous monitoring. The recent development of PV panel monitoring systems provides a modest and viable approach to monitoring and managing the condition of the PV plants. In general, conventional procedures are used to identify the faulty modules earlier and to avoid declines in power generation. The existing deep learning architectures provide the required output to predict the faulty PV panels with less accuracy and a more time-consuming process. To increase the accuracy and to reduce the processing time, a new Convolutional Neural Network (CNN) architecture is required. Hence, in the present work, a new Real-time Multi Variant Deep learning Model (RMVDM) architecture is proposed, and it extracts the image features and classifies the defects in PV panels quickly with high accuracy. The defects that arise in the PV panels are identified by the CNN based RMVDM using RGB images. The biggest difference between CNN and its predecessors is that CNN automatically extracts the image features without any help from a person. The technique is quantitatively assessed and compared with existing faulty PV board identification approaches on the large real-time dataset. The results show that 98% of the accuracy and recall values in the fault detection and classification process.

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Keywords

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