Researchers create framework to optimize solar panel tilt in extreme winds
Researchers at the Centre for Material Forming at PLS University in Sophia Antipolis, France, have proposed a numerical decision-making framework for solar panel protection against extreme weather conditions.
The research paper “Combining machine learning and computational fluid dynamics for solar panel tilt angle optimization in extreme winds,” available in the journal Physics of Fluids, says there is currently no best practice on how to stow solar panels under strong wind conditions.
Traditional methods for protecting solar panels against high winds often include panels entering a presumed safe stow position parallel to the ground once a certain wind speed is reached. While this has proved effective in some instances, the panels lose energy output in this position and are often not protected against the highest wind speeds.
The team’s new framework is designed to recommend a stow position that leverages the increasing use of solar tracker actuators and allows panels to set an optimal angle relative to the sun to continue maximizing power output.
The framework combines advanced wind simulations with machine learning to optimize individual solar panel angles under strong winds. “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,” said Elie Hachem, author of the report.
Unlike previous methods developed for panel protection, the new framework treats panels as independent decision-makers and identifies data-driven solutions to reduce stress and outperform current safeguards. “It’s like teaching the panels to dance with the wind, minimizing damage while protecting energy production during high wind speeds,” Hachem added.
The research team conducted a range of experiments testing their framework against different potential causes of breakage, including tear, vibrations and fatigue.
They found their proposed approach minimized aerodynamic efforts on two-dimensional and three-dimensional arrangements of six ground-mounted panels under an incident wind speed of 50 km/h. It was also found to outperform baseline safeguarding practices considered in the literature included in the research paper by several dozen percent.
“This gives hope that, by interacting with its computational fluid dynamics environment in a trial-and-error manner, a deep reinforcement learning agent can learn unexpected solutions to this complex decision-making problem and come up with innovative, feasible solutions capable of managing utility-scale solar assets during high-wind events while efficiently complementing engineering intuition and practical experience,” the researchers say.
They add that the framework challenges traditional engineering practices and offers a scalable solution for enhancing real-world resilience.
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