Stochastic estimation of wake-induced loads in wind farms using conformalized graph neural networks

Abstract

In the evolving landscape of sustainable energy, wind power has become a cornerstone in the transition towards renewable energy sources. With significant advancements in turbine technology and simulation methods, wind farms are now a cost-effective alternative to traditional power plants. Nevertheless, optimizing the performance and lifespan of wind turbines remains challenging, especially when it comes to predicting and managing the cumulative load on turbine structures. Wake effects, which result from the complex aerodynamic interplay between turbines, reduce energy efficiency and increase mechanical stress on turbine components. A precise assessment of wake-induced loads is therefore vital for estimating the Remaining Useful Lifetime (RUL) of turbines. To address these challenges, we introduce a novel approach using Graph Neural Networks (GNNs) in combination with Conformal Predictors to model wind farms and evaluate wake-induced loads while estimating uncertainty. GNNs are particularly adept at capturing the complex interactions in a wind farm due to their ability to model graph-structured data. This enables a more accurate representation of the aerodynamic interactions between turbines. Alongside the GNN framework, we employ Conformal Predictors for uncertainty estimation. Conformal Predictors provide statistically valid prediction sets based on past data and minimal assumptions, allowing us to estimate uncertainty bounds with improved confidence. The fusion of GNNs with Conformal Predictors offers a robust framework for predicting turbine loads, while reliably quantifying the uncertainty associated with these predictions.