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

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.

Monitoring photovoltaic soiling: assessment, challenges, and perspectives of current and potential strategies

“…Pictures can be taken from drones (Mehta et al, 2018) or satellites (Supe et al, 2020) and analyzed through standard image processing or machine-learning techniques. In the recent years, researchers have proposed or investigated various image processing methods for soiling detection from modules' pictures (Mehta et al, 2018;Qasem et al, 2017;Supe et al, 2020;Yap et al, 2015). In addition, a novel SIA approach was recently proposed by Yang et al (Yang et al, 2020a.…”

“…As mentioned earlier, the area coverage can then be linearly converted into a transmittance loss ( Figure 14 ). Pictures can be taken from drones ( Mehta et al., 2018 ) or satellites ( Supe et al., 2020 ) and analyzed through standard image processing or machine-learning techniques. In the recent years, researchers have proposed or investigated various image processing methods for soiling detection from modules' pictures ( Mehta et al., 2018 ; Qasem et al., 2017 ; Supe et al., 2020 ; Yap et al., 2015 ).…”

Impact of COVID-19 Lockdown on the Fisheries Sector: A Case Study from Three Harbors in Western India

“…Plan-etScope images are multispectral data comprising of 4-bands in the visible-infrared electromagnetic spectrum. The advantage of Planet constellation satellite over other imageries is that they can capture the same location daily with 3-meter pixel size along with 16-bit radiometric resolution [44,45]. The movement of fishing boats in the harbors during the pre-lockdown, lockdown and post-lockdown periods was monitored with the help of Planet images.…”

Impacts of changes in mangrove ecosystems in the Ba and Rewa deltas, Fiji using multi-temporal Landsat data and social survey

“…The Google Earth Engine (GEE) was used to acquire Landsat data for the study area. GEE is a cloud-based semi-automated image processing tool, capable of performing heavy operations for big data (large area as well as time-series) efficiently (Farda 2017;Tsai et al 2018;Supe et al, 2020). The target years in this study were selected for around every 10 years, to filter out the impacts induced by interannual events such as El Niño-Southern Oscillation (ENSO), average and focus more on the impacts of human activities and climate change.…”