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Title
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Photovoltaic Panels Classification Using Isolated and Transfer-Learned Deep Neural Models Using Infrared-Thermographic Images
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Sub-Title |
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Subject |
Deep convolution neural network; PV panels; infrared images; hotspots
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Author |
Waqas Ahmed, Aamir Hanif, Karam Dad Kallu, Abbas K
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Publish Year |
2021 |
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Diss#. |
https://doi.org/10.3390/s21165668 |
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Pages |
5668-5668 |
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Text Language |
English |
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Accession |
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Library Section |
Research Article |
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Abstract |
Defective PV panels reduce the efficiency of the whole PV string, causing loss of investment by
decreasing its efficiency and lifetime. In this study, firstly, an isolated convolution neural model (ICNM)
was prepared from scratch to classify the infrared images of PV panels based on their health, i.e., healthy,
hotspot, and faulty. The ICNMoccupies the leastmemory, and it also has the simplest architecture, lowest
execution time, and an accuracy of 96% compared to transfer learned pre-trained ShuffleNet, GoogleNet,
and SqueezeNet models. Afterward, ICNM, based on its advantages, is reused through transfer learning
to classify the defects of PV panels into five classes, i.e., bird drop, single, patchwork, horizontally aligned
string, and block with 97.62% testing accuracy. This proposed approach can identify and classify the PV
panels based on their health and defects faster with high accuracy and occupies the least amount of the
system’s memory, resulting in savings in the PV investmen
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