Staff View: Remote anomaly detection and classification of solar photovoltaic modules based on deep neural network

Remote anomaly detection and classification of solar photovoltaic modules based on deep neural network

Solar photovoltaic systems are being widely used in green energy harvesting recently. At the same rate of growth, the modules that come to the end of life are growing fast. The solar modules contain heavy metals such as lead, tin, and cadmium, which could pollute the environment. Inspection and main...

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Main Authors:	Minh huy Le, Van Su Luong, Dang Khoa Nguyen, Van-Duong Dao, Ngoc Hung Vu, Hong Ha Thi Vu
Format:	Bài trích
Language:	English
Published:	Sustainable Energy Technologies and Assessments 2021
Subjects:	Green energy Solar photovoltaic modules
Online Access:	https://www.sciencedirect.com/science/article/abs/pii/S2213138821005579?via%3Dihub https://dlib.phenikaa-uni.edu.vn/handle/PNK/3298 https://doi.org/10.1016/j.seta.2021.101545
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spelling	oai:localhost:PNK-32982022-08-17T05:54:46Z Remote anomaly detection and classification of solar photovoltaic modules based on deep neural network Minh huy Le Van Su Luong Dang Khoa Nguyen Van-Duong Dao Ngoc Hung Vu Hong Ha Thi Vu Green energy Solar photovoltaic modules Solar photovoltaic systems are being widely used in green energy harvesting recently. At the same rate of growth, the modules that come to the end of life are growing fast. The solar modules contain heavy metals such as lead, tin, and cadmium, which could pollute the environment. Inspection and maintenance of solar modules are important to increase the lifetime, reduce energy loss, and environmental protection. In this research, we proposed an efficient way for inspection and classification of anomaly solar modules using infrared radiation (IR) cameras and deep neural networks. The IR cameras could capture the temperature distribution on the solar modules remotely, and the deep neural networks could accurately prediction of the anomaly modules and classification of the anomaly types. We proposed a deep neural network based on a residual network structure and ensemble technique to accurately predict and classify anomaly solar modules based on the IR images. An IR images dataset on real solar farms with 20,000 images and 12 anomaly solar modules was used to verify the proposed approach. The experiment results show that the proposed model could predict an anomaly module on an average of 94% and correctly classify 12 anomaly types on an average of 86%. 2021-10-27T02:05:04Z 2021-10-27T02:05:04Z 2021 Bài trích https://www.sciencedirect.com/science/article/abs/pii/S2213138821005579?via%3Dihub https://dlib.phenikaa-uni.edu.vn/handle/PNK/3298 https://doi.org/10.1016/j.seta.2021.101545 en Sustainable Energy Technologies and Assessments
institution	Digital Phenikaa
collection	Digital Phenikaa
language	English
topic	Green energy Solar photovoltaic modules
spellingShingle	Green energy Solar photovoltaic modules Minh huy Le Van Su Luong Dang Khoa Nguyen Van-Duong Dao Ngoc Hung Vu Hong Ha Thi Vu Remote anomaly detection and classification of solar photovoltaic modules based on deep neural network
description	Solar photovoltaic systems are being widely used in green energy harvesting recently. At the same rate of growth, the modules that come to the end of life are growing fast. The solar modules contain heavy metals such as lead, tin, and cadmium, which could pollute the environment. Inspection and maintenance of solar modules are important to increase the lifetime, reduce energy loss, and environmental protection. In this research, we proposed an efficient way for inspection and classification of anomaly solar modules using infrared radiation (IR) cameras and deep neural networks. The IR cameras could capture the temperature distribution on the solar modules remotely, and the deep neural networks could accurately prediction of the anomaly modules and classification of the anomaly types. We proposed a deep neural network based on a residual network structure and ensemble technique to accurately predict and classify anomaly solar modules based on the IR images. An IR images dataset on real solar farms with 20,000 images and 12 anomaly solar modules was used to verify the proposed approach. The experiment results show that the proposed model could predict an anomaly module on an average of 94% and correctly classify 12 anomaly types on an average of 86%.
format	Bài trích
author	Minh huy Le Van Su Luong Dang Khoa Nguyen Van-Duong Dao Ngoc Hung Vu Hong Ha Thi Vu
author_facet	Minh huy Le Van Su Luong Dang Khoa Nguyen Van-Duong Dao Ngoc Hung Vu Hong Ha Thi Vu
author_sort	Minh huy Le
title	Remote anomaly detection and classification of solar photovoltaic modules based on deep neural network
title_short	Remote anomaly detection and classification of solar photovoltaic modules based on deep neural network
title_full	Remote anomaly detection and classification of solar photovoltaic modules based on deep neural network
title_fullStr	Remote anomaly detection and classification of solar photovoltaic modules based on deep neural network
title_full_unstemmed	Remote anomaly detection and classification of solar photovoltaic modules based on deep neural network
title_sort	remote anomaly detection and classification of solar photovoltaic modules based on deep neural network
publisher	Sustainable Energy Technologies and Assessments
publishDate	2021
url	https://www.sciencedirect.com/science/article/abs/pii/S2213138821005579?via%3Dihub https://dlib.phenikaa-uni.edu.vn/handle/PNK/3298 https://doi.org/10.1016/j.seta.2021.101545
_version_	1751856306038243328
score	8.891145

Remote anomaly detection and classification of solar photovoltaic modules based on deep neural network

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