Wind plays a dual role in the efficiency and safety of solar power systems. On one hand, wind helps maintain panel performance by clearing dirt and cooling surfaces, boosting efficiency. On the other hand, extreme wind events pose structural risks, potentially leading to costly damages and prolonged system downtime. As the adoption of solar power grows, so too do insurance claims linked to weather-related damage.
In Physics of Fluids, researchers from the Centre for Material Forming at PLS University in Sophia Antipolis, France, introduced an innovative numerical decision-making framework to address wind-related risks for solar panels. This solution blends computational fluid dynamics with machine learning to create smarter, adaptive systems.
"By blending advanced fluid dynamics and artificial intelligence, we saw an opportunity to address wind damage risks innovatively and contribute to the resilience of renewable energy systems," explained Elie Hachem, a co-author of the study.
Traditional wind damage mitigation strategies focus on optimizing row spacing, ground clearance, and tilt angles of solar panels. Existing tracking mounts, which rotate panels for optimal sunlight exposure, rely on a stow mode that lays panels flat to reduce damage at high wind speeds. However, this approach halts energy production and offers limited protection against extreme gusts.
The team's framework uses real-time wind simulations and machine learning to optimize the tilt angle of each panel individually, enabling them to respond dynamically to wind conditions. This novel approach, which views panels as independent decision-makers, minimizes stress and damage while maintaining energy output, outperforming current safety measures.
"It's like teaching the panels to dance with the wind, minimizing damage while protecting energy production during high wind speeds," said Hachem.
This adaptive framework challenges conventional engineering methods and provides a scalable solution for enhancing the resilience of solar power systems. By integrating advanced simulation tools and AI, it sets a new standard for renewable energy systems designed to thrive in extreme weather conditions while contributing to global carbon neutrality goals.
Research Report:Combining machine learning and computational fluid dynamics for solar panel tilt angle optimization in extreme winds
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