A digital-twin solution for floating offshore wind turbines validated using a full-scale prototype

Abstract

Abstract. In this work, we implement, verify, and validate a physics-based digital twin solution applied to a floating offshore wind turbine. The digital twin is validated using measurement data from the full-scale TetraSpar prototype. We focus on the estimation of the aerodynamic loads, wind speed, and section loads along the tower, with the aim at estimating the fatigue life-time of the tower. Our digital twin solution integrates: 1) a Kalman filter to estimate the structural states based on a linear model of the structure and measurements from the turbine, 2) an aerodynamic estimator, and 3) a physics-based virtual sensing procedure to obtain the loads along the tower. The digital twin relies on a set of measurements that are expected to be available on any existing wind turbine (power, pitch, rotor speed, and tower acceleration), and motion sensors that are likely to be standard measurements for a floating platform (inclinometers and GPS sensors). We explore two different pathways to obtain physics-based models: a suite of dedicated Python tools implemented as part of this work, or the OpenFAST linearization feature. In our final version of the digital twin, we use components from both approaches. We perform different numerical experiments to verify the individual models of the digital twin. In this simulation realm, we obtain estimated damage equivalent loads with an accuracy of the order of 5 % to 10 %. When comparing the digital twin estimations with the measurements from the TetraSpar prototype, the errors increased to 10 %–15 % on average. Overall, the accuracy of the results appears promising and demonstrates the possibility to use digital twin solutions to estimate fatigue loads on floating offshore wind turbines. A natural continuation of this work would be to implement the monitoring and diagnostics aspect of the digital twin, to inform operation and maintenance decisions. The digital twin solution is provided with examples as part of an open-source repository.

Experimental validation of a Kalman observer using linearized OpenFAST and a fully instrumented 1:70 model

“…Previous work on floating offshore wind turbine (FOWT) DTs have investigated the use of the nonlinear aero‐elastic simulator OpenFAST, 1 developed by the National Renewable Energy Lab (NREL), to model the FOWT platform in real time and make control decisions 2 . Branlard et al investigated the application of a Kalman filter and OpenFAST linearizations to estimate fatigue life of FOWT blades and tower components, observing an accuracy of 5% to 10% for damage‐equivalent load results 3 . Similar approaches have been taken for onshore turbines 4 …”

“…For real-time virtual sensing, Kalman filters have been applied to FOWT models to improve wind speed and shaft torque measurements. 3 Additional applications for controls have also been investigated numerically, 5 demonstrating the importance of including hydrodynamic states in the modeling process. Extensive work applying Kalman filter techniques for LQG control of onshore turbines has also been studied using numerical simulations.…”