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

Supe, Hitesh, Avtar, Ram, Singh, Deepak, Gupta, Ankita, Yunus, Ali P., Dou, Jie, Ravankar, Ankit A., Mohan, Geetha, Chapagain, Saroj, Sharma, Vivek, Singh, Chander K., Tutubalina, Olga and Kharrazi, Ali, (2020). Google Earth Engine for the Detection of Soiling on Photovoltaic Solar Panels in Arid Environments. Remote Sensing, 12(9), 1-26

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    Author Supe, Hitesh
    Avtar, Ram
    Singh, Deepak
    Gupta, Ankita
    Yunus, Ali P.
    Dou, Jie
    Ravankar, Ankit A.
    Mohan, Geetha
    Chapagain, Saroj
    Sharma, Vivek
    Singh, Chander K.
    Tutubalina, Olga
    Kharrazi, Ali
    Title Google Earth Engine for the Detection of Soiling on Photovoltaic Solar Panels in Arid Environments
    Appearing in Remote Sensing
    Volume 12
    Issue No. 9
    Publication Date 2020-05-05
    Place of Publication Basel
    Publisher MDPI
    Start page 1
    End page 26
    Language eng
    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.
    Keyword land surface temperature
    normalized differential sand index
    soiling of solar panels
    Copyright Holder The Authors
    Copyright Year 2020
    Copyright type Creative commons
    DOI 10.3390/rs12091466
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    Created: Tue, 17 Nov 2020, 15:04:44 JST by Rachel Nunn on behalf of UNU IAS