Google Earth Engine for the Detection of Soiling on Photovoltaic Solar Panels in Arid Environments
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. T...
| Main Authors: | , , , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2020-05-01
|
| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/12/9/1466 |
| _version_ | 1797568737506754560 |
|---|---|
| author | Hitesh Supe Ram Avtar Deepak Singh Ankita Gupta Ali P. Yunus Jie Dou Ankit A. Ravankar Geetha Mohan Saroj Kumar Chapagain Vivek Sharma Chander Kumar Singh Olga Tutubalina Ali Kharrazi |
| author_facet | Hitesh Supe Ram Avtar Deepak Singh Ankita Gupta Ali P. Yunus Jie Dou Ankit A. Ravankar Geetha Mohan Saroj Kumar Chapagain Vivek Sharma Chander Kumar Singh Olga Tutubalina Ali Kharrazi |
| author_sort | Hitesh Supe |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T20:01:11Z |
| format | Article |
| id | doaj.art-4bcfa3e9d7ec462d84b96637a5afbe46 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T20:01:11Z |
| publishDate | 2020-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-4bcfa3e9d7ec462d84b96637a5afbe462023-11-19T23:32:42ZengMDPI AGRemote Sensing2072-42922020-05-01129146610.3390/rs12091466Google Earth Engine for the Detection of Soiling on Photovoltaic Solar Panels in Arid EnvironmentsHitesh Supe0Ram Avtar1Deepak Singh2Ankita Gupta3Ali P. Yunus4Jie Dou5Ankit A. Ravankar6Geetha Mohan7Saroj Kumar Chapagain8Vivek Sharma9Chander Kumar Singh10Olga Tutubalina11Ali Kharrazi12Graduate School of Environmental Earth Science, Hokkaido University, Sapporo 060-0810, JapanGraduate School of Environmental Earth Science, Hokkaido University, Sapporo 060-0810, JapanDepartment of Geography and Resource Management, The Chinese University of Hong Kong, Sha Tin, New Territories, Hong Kong, ChinaGraduate School of Information Science and Technology, Hokkaido University, Sapporo 060-0814, JapanState Key Laboratory of Geohazard Prevention and Geoenvironment Protection, Chengdu University of Technology, Chengdu 610059, ChinaDepartment of Civil and Environmental Engineering, Nagaoka University of Technology, Nagaoka, Niigata 940-2188, JapanDivision of Human Mechanical Systems and Design, Faculty of Engineering, Hokkaido University, Sapporo, Hokkaido 060-8628, JapanInstitute for the Advanced Study of Sustainability, United Nations University (UNU-IAS), Tokyo 150-8925, JapanInstitute for the Advanced Study of Sustainability, United Nations University (UNU-IAS), Tokyo 150-8925, JapanRajasthan Renewable Energy Corporation Limited (RRECL), Jaipur 302001, Rajasthan, IndiaDepartment of Energy and Environment, TERI School of Advanced Studies, New Delhi 110070, IndiaFaculty of Geography, Moscow State University, Leninskiye Gory, 119991 Moscow, RussiaAdvanced Systems Analysis Group, International Institute for Applied Systems Analysis, Schlossplatz 1, A-2361 Laxenburg, AustriaThe 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.https://www.mdpi.com/2072-4292/12/9/1466land surface temperaturenormalized differential sand indexsoiling of solar panels |
| spellingShingle | Hitesh Supe Ram Avtar Deepak Singh Ankita Gupta Ali P. Yunus Jie Dou Ankit A. Ravankar Geetha Mohan Saroj Kumar Chapagain Vivek Sharma Chander Kumar Singh Olga Tutubalina Ali Kharrazi Google Earth Engine for the Detection of Soiling on Photovoltaic Solar Panels in Arid Environments Remote Sensing land surface temperature normalized differential sand index soiling of solar panels |
| title | Google Earth Engine for the Detection of Soiling on Photovoltaic Solar Panels in Arid Environments |
| title_full | Google Earth Engine for the Detection of Soiling on Photovoltaic Solar Panels in Arid Environments |
| title_fullStr | Google Earth Engine for the Detection of Soiling on Photovoltaic Solar Panels in Arid Environments |
| title_full_unstemmed | Google Earth Engine for the Detection of Soiling on Photovoltaic Solar Panels in Arid Environments |
| title_short | Google Earth Engine for the Detection of Soiling on Photovoltaic Solar Panels in Arid Environments |
| title_sort | google earth engine for the detection of soiling on photovoltaic solar panels in arid environments |
| topic | land surface temperature normalized differential sand index soiling of solar panels |
| url | https://www.mdpi.com/2072-4292/12/9/1466 |
| work_keys_str_mv | AT hiteshsupe googleearthengineforthedetectionofsoilingonphotovoltaicsolarpanelsinaridenvironments AT ramavtar googleearthengineforthedetectionofsoilingonphotovoltaicsolarpanelsinaridenvironments AT deepaksingh googleearthengineforthedetectionofsoilingonphotovoltaicsolarpanelsinaridenvironments AT ankitagupta googleearthengineforthedetectionofsoilingonphotovoltaicsolarpanelsinaridenvironments AT alipyunus googleearthengineforthedetectionofsoilingonphotovoltaicsolarpanelsinaridenvironments AT jiedou googleearthengineforthedetectionofsoilingonphotovoltaicsolarpanelsinaridenvironments AT ankitaravankar googleearthengineforthedetectionofsoilingonphotovoltaicsolarpanelsinaridenvironments AT geethamohan googleearthengineforthedetectionofsoilingonphotovoltaicsolarpanelsinaridenvironments AT sarojkumarchapagain googleearthengineforthedetectionofsoilingonphotovoltaicsolarpanelsinaridenvironments AT viveksharma googleearthengineforthedetectionofsoilingonphotovoltaicsolarpanelsinaridenvironments AT chanderkumarsingh googleearthengineforthedetectionofsoilingonphotovoltaicsolarpanelsinaridenvironments AT olgatutubalina googleearthengineforthedetectionofsoilingonphotovoltaicsolarpanelsinaridenvironments AT alikharrazi googleearthengineforthedetectionofsoilingonphotovoltaicsolarpanelsinaridenvironments |