Transfer Learning in Graph Neural Networks with Real-World Offshore Wind Farm Data

Jan Van Rompaey, Francisco de Nolasco Santos, W. Weijtjens, C. Devriendt

The ever-growing need for renewable energy has driven the development of increasingly large offshore wind turbines. Alongside improved design codes and changing control strategies, this has led to fatigue becoming an operational concern. Farm operators require information about the impact of their decisions (e.g. curtailment) on the structural reserve of each turbine, which necessitates a tool that can predict both quickly and for yet unseen situations. Current approaches rely either on numerical simulations, which are too slow, or data-driven methodologies, which often suffer from data sparsity and limited generalization capabilities. Therefore, our goal is to develop a surrogate model that enables real-time control through rapid inference while improving extrapolation capabilities. To this end, we propose the use of a Graph Neural Network (GNN). GNNs are ideally suited to interpret and learn from interdependent non-Euclidean systems such as wind farms. They are able to process many different farm lay-outs, meaning they can learn to capture the underlying relations between the variables and the positions of the turbines. When the model is pretrained on data from one kind of farm and later fine-tuned on data from another, valuable knowledge can be transferred between both cases in a process called Transfer Learning. This has been successfully implemented on wind farms in previous work, but for simulated data only. In this contribution, we will extend this approach to real-world data. First, the network is trained on a large collection of generated farm layouts with global wind inflow conditions (wind speed, wind direction and turbulence intensity) drawn from their respective distributions while simulated predictions are obtained using PyWake, an open-source wind farm simulation tool capable of computing wake effects and power production of individual turbines. The goal is to predict local (i.e per-turbine) wind conditions and power as well as fore-aft (FA) and side-side (SS) damage equivalent moment (DEM). Afterwards, the model will be fine-tuned on real-world data from instrumented turbines in a North Sea wind farm. Farm-wide DEMs (our ground truth) are obtained as machine learning model predictions using accelerometer data. Multiple transfer learning techniques will be compared, including layer freezing and low rank adaptation (LoRA), while it will be investigated how to minimize negative transfer. Predictions from models obtained with TL will be contrasted with those obtained from models trained either from scratch or without TL to expose its benefits. Additionally, predictions on unseen data will further determine whether effective generalization has taken place, indicating the underlying fundamental physics has been captured and been successfully transferred.