@Article{fdmp.2026.077648,
AUTHOR = {Lyu Guanghua, Dingxiao Jiao, Abdulrahman AlKassem, Dakan Ying, Rizwan Arshad, Jiahua Ni, Zhe Liu, Syed Hadi Hussain Shah},
TITLE = {Autonomous UAV Swarm Maintenance for Dust and Hotspot Control in Photovoltaic Farms},
JOURNAL = {Fluid Dynamics \& Materials Processing},
VOLUME = {},
YEAR = {},
NUMBER = {},
PAGES = {{pages}},
URL = {http://www.techscience.com/fdmp/online/detail/26563},
ISSN = {1555-2578},
ABSTRACT = {Desert photovoltaic, PV, installations experience significant efficiency losses due to dust accumulation, which also promotes localized overheating, known as hotspots, caused by uneven solar irradiance and partial cell shading. These hotspots can accelerate material degradation and increase the risk of permanent panel damage. This study presents an autonomous maintenance strategy based on a cooperative swarm of unmanned aerial vehicles, UAVs, enabling contactless dust removal and active hotspot cooling. The approach combines high-fidelity computational fluid dynamics to characterize aerodynamic downwash for effective dust detachment with fluid–structure interaction analysis to verify the structural integrity of PV panels under induced loads. A fractional-order PID controller minimizes UAV energy consumption, while a model predictive control framework coordinates real-time task allocation within the swarm. Numerical and experimental results indicate dust removal efficiencies exceeding 92%, with maximum panel stresses of 18.5 MPa and deflections remaining within safe mechanical limits. The system achieves a net positive energy balance, recovering more than 10 kWh per mission. Evidence is provided that the proposed integrated CFD–FSI modeling framework, energy-aware swarm coordination strategy, and Pareto-optimized control architecture collectively provide a robust and scalable solution for enhancing the reliability and resilience of desert solar farms.},
DOI = {10.32604/fdmp.2026.077648}
}