Machine Learning

Predicting the Unpredictable: Machine Learning Tool Forecasts Wind Turbine Blade Erosion

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The wind energy industry is growing rapidly as a response to the global call to combat climate change by rapidly decarbonising the energy sector. However, with the increasing deployment of wind turbines in offshore locations, they are exposed to harsh environmental conditions which can lead to leading edge erosion on the blades. This erosion can […]

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The Future is blowing in the wind: Machine Learning takes on the Damage Detection in Composite Materials used in Wind Turbine Blades

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Wind turbines are a crucial source of renewable energy, but their blades are constantly exposed to harsh weather conditions, which can cause damage over time. To ensure the safety and efficiency of wind turbines, it is essential to detect damage in the composite materials used to make the blades. However, detecting damage in composites can

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Revolutionizing Wind Farm Efficiency: New Machine Learning Technique Predicts Optimal Layouts in Seconds

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Researchers have developed a novel machine learning and pattern recognition approach to solving the wind farm layout optimization problem (WFLOP). The traditional row-by-row grids of turbines seen in many wind farms today can suffer from a 10-15% annual energy production loss due to inter-turbine turbulence called the wake effect. To minimize this loss, an optimal

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