Structural prowess in solar panel cleaning: A comparative study of robotic designs

• Published in: Journal of Applied Research and Technology
• Main Author: Murshiduzzamana; As'Arry A.; Keong L.W.; Yussof H.; Wan Hasan W.Z.
• Format: Article
• Language: English
• Published: Universidad Nacional Autonoma de Mexico 2024
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85204711318&doi=10.22201%2ficat.24486736e.2024.22.4.2440&partnerID=40&md5=3e8bdb40730881c0ec1cecedc1fdbbb4

Automated solar panel cleaning robots have emerged as a solution to mitigate the adverse effects of dust accumulation on solar panels, which can impede energy production. However, concerns persist regarding the potential long-term damage to panels and the efficiency of cleaning methods. This research focuses on various automated cleaning robots, evaluated with a primary emphasis on their structural design and its impact on cleaning efficiency and safety. The robots are assessed based on their cleaning motion time and the load stress exerted on photovoltaic (PV) panels. To evaluate structural integrity, Ansys simulations are employed to assess the strength of solar panels and frames under the loads exerted by different robot types. Furthermore, the cleaning motion of the robot is simulated using SolidWorks, with predefined pathways. The results of this study highlight the crucial role of structural design in the context of solar panel cleaning robotics. Specifically, single axis robot is identified as a standout performer, exerting only 4% stress on PV panels among the considered 4 types of robots in comparison with the maximum stress applying robot and exhibiting the fastest cleaning motion of 38 seconds only for the specified panels which is 4 times faster than other compared robots. These insights provide valuable guidance for further advancements in the design and operation of automated solar panel cleaning systems, emphasizing the significance of structural considerations in enhancing the overall efficiency and effectiveness of these robots. © 2024 Universidad Nacional Autonoma de Mexico. All rights reserved.