Dust InSMS: Intelligent soiling measurement system for dust detection on solar mirrors using computer vision methods. (January 2023) - El Ydrissi, Massaab; Ghennioui, Hicham; Ghali Bennouna, El; Alae, Azouzoute; Abraim, Mounir; Taabane, Ibrahim; Farid, Abdi
Highlights: A new method for soiling quantification is proposed based on CNN approach. Experimental measurements of a Fresnel solar field are conducted to collect data. The innovation, software and hardware of Dust InSMS sensor are described. A good agreement is proven during outdoor validation and tests of the Dust InSMS. An optimal cleaning scenario is developed based on genetic algorithms. Abstract: The dust accumulation strongly impacts the optical efficiency of solar concentrators, in particular the reflectivity of solar mirrors. Therefore, reducing the impact of reflectivity losses due to soiling and optimizing cleaning strategy are key factors. In this paper, the impact of dust accumulation on the reflectivity parameter of Fresnel mirrors is studied at the GEP research platform during the dry period. Based on the collected data, a new system for dust detection is proposed based on the classification approach using the convolutional neural networks and image processing algorithms in which no similar work is presented in the literature that uses the same approach to quantify the soiling phenomenon on CSP mirrors. The test loss and accuracy obtained by the proposed model are respectively 0.28 and 0.96. The outdoor validation results obtained so far suggest that the Dust InSMS concept and method could be a promising efficient and low-cost sensor. As the proposed system performs the GPS coordinates for each measurement, an optimal cleaning scenario is developed based on genetic algorithms to optimize the cleaning scenario and to come up with the shortest cleaning path.