Online condition monitoring of floating wind turbines drivetrain by means of digital twin

Farid K Moghadam,Amir R Nejad

doi:10.1016/j.ymssp.2021.108087

Farid K Moghadam, Amir R Nejad

Open Access

https://doi.org/10.1016/j.ymssp.2021.108087

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Abstract

This paper presents a digital twin (DT) condition monitoring approach for drivetrains on floating offshore wind turbines. Digital twin in this context consists of torsional dynamic model, online measurements and fatigue damage estimation which is used for remaining useful life (RUL) estimation. At first, methods for system parameter estimation are presented. The digital twin model provides sufficient inputs for the load observers designed in specific points of the drivetrain to estimate the online load and subsequently stress in the different components. The estimated real-time stress values feed the degradation model of the components. The stochastic degradation model proposed for estimation of real-time fatigue damage in the components is based on a proven model-based approach which is tested under different drivetrain operations, namely normal, faulty and overload conditions. The uncertainties in model, measurements and material properties are addressed, and confidence interval for the estimations is provided by a detailed analysis on the signal behavior and using Monte Carlo simulations. A test case, using 10 MW drivetrain, has been demonstrated.

Highlights

IntroductionThere are yet limited experiences with floating offshore wind turbines (FWT) to estimate the actual operating ex penditures (OPEX)
In order to realize EU’s goal of climate neutrality by 2050, the EU strategy is that 22% of electricity demand in Europe (300GW/1361GW = 0.22) should be generated by offshore wind by 2050, [1,2]
The results of an investigation performed al. [4] emphasizes on the operating ex penditures (OPEX) as the main contributor to this gap, so that OPEX in bottom-fixed offshore wind turbines is based on another study [5] in average twice higher than the land-based turbines

Summary

Introduction

There are yet limited experiences with floating offshore wind turbines (FWT) to estimate the actual OPEX. The motivation of this research is reducing OPEX and subsequently LCOE in FWTs by increasing the wind turbine availability by means of performing predictive maintenance of the turbine critical components and the subsequent reduction of unexpected main tenance and expensive offshore transport/operation cost. The latter is realized through online monitoring based on computationally inexpensive digital twin (DT) models and the subsequent dynamic optimization of the turbine overhaul plan and scheduled mainte nance intervals. Power train system including rotor, main bearings, gearbox, generator and power converter accounts for 57% of turbine total failures (http://creativecommons.org/licenses/by/4.0/)

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