Long-term fatigue estimation on offshore wind turbines interface loads through loss function physics-guided learning of neural networks

• Published in: Renewable energy Vol. 205; pp. 461 - 474
• Main Authors: de N Santos, Francisco, D’Antuono, Pietro, Robbelein, Koen, Noppe, Nymfa, Weijtjens, Wout, Devriendt, Christof
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.03.2023
• Subjects: Offshore wind; Physics-informed machine learning; Structural health monitoring; Physics-informed machine learning; Offshore wind; Structural health monitoring
• ISSN: 0960-1481; 1879-0682
• DOI: 10.1016/j.renene.2023.01.093

Offshore wind turbines are exposed during their serviceable lifetime to a wide range of loads from aero-, hydro- and structural dynamics. This complex loading scenario will have an impact on the lifetime of the asset, with fatigue remaining the key structural design driver for the substructure, e.g. the monopile. The ability to monitor the progression of fatigue life of these assets has recently become an operational concern. To achieve a monitoring alternative to strain gauges (cost-prohibitive farm-wide installation), supervisory control and data acquisition (SCADA) systems, often coupled with acceleration measurements, have been used. Existing work focused primarily on ten-minute fatigue load estimation. However, fatigue accumulates over time and the ability to accurately monitor this accumulation of fatigue over a longer time-window is paramount. In this contribution we investigate a novel approach using nine months of real-world SCADA and acceleration ten-minute statistics as inputs of a neural network model for long-term DEM estimation. This is further enhanced by including physical information relative to the problem at hand into the neural network model, in a so-called physics-informed machine learning approach. Specifically, we employ a custom loss function – the Minkowski logarithmic error – which prioritizes conservativeness (over-prediction of fatigue rates) and to embed the damage accumulation into the machine learning model. In the results and discussion section, we use Tower-TP interface load measurements for three real-world turbines to demonstrate the concept. First a model is trained on one turbine, before being applied to all three locations. The model performance is compared to direct measurements of fatigue progression at all three locations and a control loss function (the mean squared logarithm error) in both the Fore-Aft and Side-Side loading directions. The novel physics-informed model clearly outperforms the control loss function in accumulated fatigue predictions with errors below 3% on accumulated damage equivalent moment estimates. Additionally, the model’s long-term performance is checked according to varying timescales (hourly, daily, weekly or monthly accumulation of DEM) and it is seen that the spread on the error rapidly reduces and converges to zero. Finally, the long-term DEM is accumulated into a monthly accumulated fatigue damage. We also see how the error on this begins to converge after 6+ months, which opens the door for extrapolation. •RUL/fatigue estimation on XL monopiles of OWTs using 9 months of real data.•SCADA and acceleration data as alternatives to strain gauge measurements.•Physics-guided learning of neural networks greatly increases model performance.•SCADA and acceleration data can be used for long-term fatigue damage estimation.•Errors on DEM are kept below 3% and on damage below 15%.