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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| Tác giả chính: | , , , , , |
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| Định dạng: | Bài trích |
| Ngôn ngữ: | English |
| Nhà xuất bản: |
Sustainable Energy Technologies and Assessments
2021
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| Chủ đề: | |
| Truy cập trực tuyến: | 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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| Tóm tắt: | 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%. |
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