Researchers have introduced an innovative numerical decision-making framework designed to enhance the protection of solar panels from severe weather conditions. This approach integrates cutting-edge wind simulations with machine learning technology to optimize the angles of solar panels during high winds. Unlike earlier strategies, this novel method considers each panel as an independent decision-maker, enabling it to discover inventive, data-driven strategies for minimizing stress and significantly exceeding the effectiveness of existing protective measures.
The solar energy sector is experiencing the fastest growth globally. Solar photovoltaic power plants transform sunlight into electricity, and their immense capacity for generating clean, renewable energy is crucial to the Net Zero Emissions by 2050 initiative, aiming to eliminate carbon dioxide emissions by 2050.
Wind can impact solar power systems in both positive and negative ways. It helps improve the performance of solar panels by clearing away dirt and debris, and since solar panels typically perform worse at higher temperatures, the airflow provided by wind enhances cooling and increases efficiency. However, the slender design of solar panels makes them susceptible to damage during high-wind events, leading to potential structural failures that can require extensive repairs. Additionally, as the use of solar energy rises, so too have the insurance claims related to damage from severe weather affecting photovoltaic panels.
In Physics of Fluids, published by AIP Publishing, researchers from the Centre for Material Forming at PLS University in Sophia Antipolis, France, have presented a groundbreaking numerical decision-making framework aimed at safeguarding solar panels from extreme weather.
“By combining advanced fluid dynamics with artificial intelligence, we recognized an innovative opportunity to tackle the risks posed by wind damage and enhance the resilience of renewable energy systems,” said author Elie Hachem.
Research teams have traditionally concentrated on factors such as row spacing, ground clearance, and tilt angles to mitigate wind damage to solar panels. While tracking mounts allow panels to pivot for optimal sunlight exposure, they only remain operational until a certain wind threshold is reached, at which point the panel shifts into a safe stow position parallel to the ground. Although this method is effective in certain scenarios, it results in energy loss and fails to fully protect the panels in the face of stronger winds.
The researchers’ framework employs advanced wind simulations alongside machine learning to adjust the angles of individual solar panels during strong winds. This innovative approach treats each panel as an independent decision-maker and generates novel, data-driven solutions to alleviate stress, significantly surpassing existing protective measures.
“It’s akin to teaching the panels how to move gracefully with the wind, reducing damage while still safeguarding energy output during high wind conditions,” Hachem explained.
This decision-making framework redefines conventional engineering methods for solar panel protection. It provides a scalable solution that enhances real-world resilience while paving the way for more intelligent, adaptive systems aimed at achieving net-zero carbon emissions in energy production.
