Results of fatigue measurement campaign on XL monopiles and early predictive models

In the present contribution, data from a measurement campaign on XL monopiles (with 9.5 MW turbines and water depths of up to 36 m) is presented. This campaign is based on data collected by three types of sensors: strain gauges (installed at the TP-tower interface), accelerations (taken at bottom, mid and upper levels of the tower) and SCADA data, comprising of wind speed, yaw angle, power, pitch angle and rpm. Additionally, wave and tidal data from a public Flemish maritime weather database is added. In a comparative analysis of XL monopiles' and standard monopiles it was seen that the fatigue behaviour of the former radically departs from the latter, with side-to-side damage surpassing fore-aft for nominal operational conditions, and standstill fatigue damage generally being bigger than nominal. Furthermore, following a methodology described in previous OWI-Lab research, artificial neural network models are trained using the aforementioned sensor data to estimate the fore-aft and side-side tower bending moment damage equivalent loads (DEL), through the use of three months worth of data with a comparative analysis ensuing. Finally, the models' performance is investigated for concrete operating conditions by correlating it with environmental and operating conditions (EOCs).