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Google Earth Engine for the Detection of Soiling on Photovoltaic Solar Panels in Arid Environments

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Title: Google Earth Engine for the Detection of Soiling on Photovoltaic Solar Panels in Arid Environments
Authors: Supe, Hitesh Browse this author
Avtar, Ram Browse this author →KAKEN DB
Singh, Deepak Browse this author
Gupta, Ankita Browse this author
Yunus, Ali P. Browse this author
Dou, Jie Browse this author
Ravankar, Ankit A. Browse this author
Mohan, Geetha Browse this author →KAKEN DB
Chapagain, Saroj Kumar Browse this author
Sharma, Vivek Browse this author
Singh, Chander Kumar Browse this author
Tutubalina, Olga Browse this author
Kharrazi, Ali Browse this author
Keywords: land surface temperature
normalized differential sand index
soiling of solar panels
Issue Date: May-2020
Publisher: MDPI
Journal Title: Remote Sensing
Volume: 12
Issue: 9
Start Page: 1466
Publisher DOI: 10.3390/rs12091466
Abstract: The soiling of solar panels from dry deposition affects the overall efficiency of power output from solar power plants. This study focuses on the detection and monitoring of sand deposition (wind-blown dust) on photovoltaic (PV) solar panels in arid regions using multitemporal remote sensing data. The study area is located in Bhadla solar park of Rajasthan, India which receives numerous sandstorms every year, carried by westerly and north-westerly winds. This study aims to use Google Earth Engine (GEE) in monitoring the soiling phenomenon on PV panels. Optical imageries archived in the GEE platform were processed for the generation of various sand indices such as the normalized differential sand index (NDSI), the ratio normalized differential soil index (RNDSI), and the dry bare soil index (DBSI). Land surface temperature (LST) derived from Landsat 8 thermal bands were also used to correlate with sand indices and to observe the pattern of sand accumulation in the target region. Additionally, high-resolution PlanetScope images were used to quantitatively validate the sand indices. Our study suggests that the use of freely available satellite data with semiautomated processing on GEE can be a useful alternative to manual methods. The developed method can provide near real-time monitoring of soiling on PV panels cost-effectively. This study concludes that the DBSI method has a comparatively higher potential (89.6% Accuracy, 0.77 Kappa) in the detection of sand deposition on PV panels as compared to other indices. The findings of this study can be useful to solar energy companies in the development of an operational plan for the cleaning of PV panels regularly.
Rights: http://creativecommons.org/licenses/by/4.0/
Type: article
URI: http://hdl.handle.net/2115/79143
Appears in Collections:環境科学院・地球環境科学研究院 (Graduate School of Environmental Science / Faculty of Environmental Earth Science) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)

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