Effect of dust cleaning on the enhancement of the power generation of a coastal PV-power plant at Zahrani Lebanon

The generation of electrical energy with photovoltaic modules is a highly useful and environmentally friendly method. For this reason, studies to increase the electrical energy production from photovoltaic (PV) modules have gained great importance. Concentrated PV systems constitute a significant part of these studies. The major problems with the concentrated PV systems are the risks of lowering the efficiency of the cells (i.e., concentration process increases PV cell temperature) and the thermal damage that can occur with sudden temperature increases. In order to avoid these risks, various applications are used to cool concentrated PV modules. In this study, an active system, which was developed for cleaning and cooling PV modules, was tested. The aim of the present study was to ensure that the surfaces were clean and free of external, contaminating factors such as dust and dirt, and that the PV cells were cooled. During the experiments, two different systems were compared: the system with the cleaning‐cooling processes and the one without these processes. Prior to starting experiments, a hydrophobic liquid onto the surfaces of the PV modules was applied to facilitate the cleaning process. The results of the experiments revealed that the temperature of the PV module was 50°C in the cleaning‐cooling process and 67°C in the system without the cleaning‐cooling process. On the other hand, it was observed that the proposed design increased the power output of PV module up to 40%.

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“…The authors 23 reported an average annual increase of 32.27% in the energy production of the solar panel as a result of the cleaning process.…”

Section: Hammoud Et Al 23mentioning

confidence: 99%

Performance assessment of a novel design concentrated photovoltaic system coupled with self‐cleaning and cooling processes

Acar

Gürel

Ergün

et al. 2020

Env Prog and Sustain Energy

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“…The environmental impacts include changes in local microclimate [7][8][9][10][11], changes in albedo [1,12,13], changes in vegetation cover [14,15], land use and land cover change [16,17], and impacts on habitat biodiversity [18,19]. The construction of PV power plants in various landscapes, such as desert, mountain, coast, lake [20][21][22], has also led to the differences in their environmental effects. Researchers need to urgently evaluate these effects and issues with the rapidly growing PV power plants [23].…”

Section: Introductionmentioning

confidence: 99%

Texture Is Important in Improving the Accuracy of Mapping Photovoltaic Power Plants: A Case Study of Ningxia Autonomous Region, China

et al. 2021

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Photovoltaic (PV) technology is becoming more popular due to climate change because it allows for replacing fossil-fuel power generation to reduce greenhouse gas emissions. Consequently, many countries have been attempting to generate electricity through PV power plants over the last decade. Monitoring PV power plants through satellite imagery, machine learning models, and cloud-based computing systems that may ensure rapid and precise locating with current status on a regional basis are crucial for environmental impact assessment and policy formulation. The effect of fusion of the spectral, textural with different neighbor sizes, and topographic features that may improve machine learning accuracy has not been evaluated yet in PV power plants’ mapping. This study mapped PV power plants using a random forest (RF) model on the Google Earth Engine (GEE) platform. We combined textural features calculated from the Grey Level Co-occurrence Matrix (GLCM), reflectance, thermal spectral features, and Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI), and Modified Normalized Difference Water Index (MNDWI) from Landsat-8 imagery and elevation, slope, and aspect from Shuttle Radar Topography Mission (SRTM) as input variables. We found that the textural features from GLCM prominent enhance the accuracy of the random forest model in identifying PV power plants where a neighbor size of 30 pixels showed the best model performance. The addition of texture features can improve model accuracy from a Kappa statistic of 0.904 ± 0.05 to 0.938 ± 0.04 and overall accuracy of 97.45 ± 0.14% to 98.32 ± 0.11%. The topographic and thermal features contribute a slight improvement in modeling. This study extends the knowledge of the effect of various variables in identifying PV power plants from remote sensing data. The texture characteristics of PV power plants at different spatial resolutions deserve attention. The findings of our study have great significance for collecting the geographic information of PV power plants and evaluating their environmental impact.

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“…However, this module is not self-powered and requires a guidance rail. In [32], a robotic cleaner was presented with three motors for the power generation of a coastal PV-power plant at Zahrani Lebanon. The dry cleaning was presented in [33], and the results shows highly effective and nonabrasive cleaning.…”

Section: Introductionmentioning

confidence: 99%

Robotic dry cleaner for photovoltaic solar panels: an implemented design that evaluated in iraq's weather

2021

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Arabian desert areas are suffered from high mitigation in the produced photovoltaic (PV) power due to high dusty weather. This article presents a robotic cleaner that will significantly reduce the impact of dust on the installed PV systems in these areas. The proposed robotic cleaner is simple, low cost, standalone, self-powered, portable, and connected to the cloud. ESP32 used as a controller that manages the cleaning process and monitors its PV power production, the battery's state of charge, time of the day, and weather conditions. Thanks to the ESP32 features and its ability to connect to the cloud, as an internet of things (IoT), via the ThingSpeak website. All the electrical, mechanical, and electronic design aspects are presented and implemented in this article. The results show the effectiveness and performance enhancement due to periodic cleaning using the proposed robotic cleaner. The results also show that the total percentage of the monthly normalized accumulated losses for the two scheduled cleaning photovoltaic strings with a performance improvement of 15.54% for the weekly cleaned string (WCS) 83.04% for the never cleaned string (NCS) through the tested month.

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Effect of dust cleaning on the enhancement of the power generation of a coastal PV-power plant at Zahrani Lebanon

Cited by 68 publications

References 13 publications

Performance assessment of a novel design concentrated photovoltaic system coupled with self‐cleaning and cooling processes

Performance assessment of a novel design concentrated photovoltaic system coupled with self‐cleaning and cooling processes

Texture Is Important in Improving the Accuracy of Mapping Photovoltaic Power Plants: A Case Study of Ningxia Autonomous Region, China

Robotic dry cleaner for photovoltaic solar panels: an implemented design that evaluated in iraq's weather

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