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A deep convolutional encoder-decoder architecture for autonomous fault detection of PV plants using multi-copters
2021
Solar Energy
A B S T R A C T This study presents an autonomous fault detection method for a wide range of common failures and defects which are visually visible on PV modules. In this paper, we focus especially on detection of bird's drops as a very typical defect on the PV modules. As a crucial prerequisite, a data-set of aerial imageries of the PV strings affected by bird's drops were collected through several experimental flight by multi-copters in order to train an accurate fully convolutional deep
doi:10.1016/j.solener.2021.05.029
fatcat:6iv5rm5lkbch5onzziqi5jkhs4