Daniela Caselli: Beckett’s Dantes: Intertextuality in the Fiction and Criticism.

On‐site imaging of modules in photovoltaic (PV) systems requires contact‐free techniques with high throughput and low cost for commercial relevance. Photoluminescence imaging satisfies these requirements, but it has so far not been used for aerial imaging. Such a system faces unique engineering and operating challenges, including the need to mount a light source on the drone and identifying module defects from images taken under low‐ and nonuniform irradiance. Herein, in‐house developed photoluminescence aerial imaging (PLAI) setup is presented and it is demonstrated that it can be used to identify defects even with a difference of excitation intensity of up to 50%. The setup consists of a hexacopter aerial drone equipped with an illumination unit and a near‐infrared camera. The unit is capable of partially illuminating full‐sized modules at night and capturing the photoluminescence response. In the maiden flight, a throughput of 13.6 PV modules per minute is achieved, and a throughput of 300 PV modules per minute is feasible. It is shown that the setup can be used to detect and identify cracks and potential induced degradation with high levels of confidence. These findings are verified by cross correlation and comparing captured photoluminescence images to electroluminescence images taken indoors.

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“…Summarized, the pixels in CC images are calculated with the reconstructed images of mean EL intensity

\bar{E L}

and mean PL intensity

\bar{P L}

values of each PV cell with the coordinates x , y in percentage of the image dimensions, according to ref. [18]

$$C C \left[\right. x , y \left]\right.

…”

Section: Resultsmentioning

confidence: 99%

“…Summarized, the pixels in CC images are calculated with the reconstructed images of mean EL intensity EL and mean PL intensity PL values of each PV cell with the coordinates x, y in percentage of the image dimensions, according to ref. [18] CC½x, y ¼ 1…”

Section: Analysis Of Pl Videosmentioning

confidence: 99%