Researchers at the Centre for Material Forming at PLS University in Sophia Antipolis in France, have proposed a unique numerical decision-making framework to protect solar panels from extreme weather conditions. This innovative method combines advanced wind simulations with artificial intelligence and machine learning to optimize individual solar panel angles under strong winds.
Unlike previous methods, this new approach treats panels as independent decision-makers and identifies creative, data-driven solutions to reduce stress, significantly outperforming current safeguards.
In the fight against climate change, solar power is currently the fastest-growing energy sector worldwide. Solar photovoltaic power plants convert sunlight into electricity, making solar power a cornerstone of the NetZero Emissions by 2050 initiative, which seeks to cut carbon dioxide emissions to zero by the year 2050.
Wind helps maintain solar panel performance by eliminating the buildup of dirt and dust, and because solar panels typically lose efficacy as they heat up, the flow of wind over and around the surface of panels increases cooling and efficiency. However, the slender panels are vulnerable to high-wind events, even to the extent of structural collapse and failures that can take weeks to repair.
To tackle these challenges, a research team led by Elie Hachem, a professor at PSL University and head of the Computing and Fluids Research Group, utilized machine learning and artificial intelligence to create more intelligent panels capable of independently making decisions to minimize damage. This technology aims to reduce interruptions for renewable energy sources, especially during more frequent extreme weather conditions.
“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 author Elie Hachem in a press release. She added, “It’s like teaching the panels to dance with the wind, minimizing damage while protecting energy production during high wind speeds.”
The framework used by the team combines advanced wind simulations with machine learning to optimize individual solar panel angles under strong winds. In contrast to earlier methods designed to shield the panels, this innovative approach views each panel as an independent decision-maker and discovers inventive, data-driven strategies to minimize stress, significantly surpassing the effectiveness of existing protective measures.
Sources:
[1] Training solar panels to dance with the wind https://www.sciencedaily.com/releases/2024/12/241217130902.htm
[2] Training solar panels to dance with the wind
