Digital twins of the mooring line tension for floating offshore wind turbines to improve monitoring, lifespan, and safety

Abstract

The number of installed floating offshore wind turbines (FOWTs) has doubled since 2017, quadrupling the total installed capacity, and is expected to increase significantly over the next decade. Consequently, there is a growing consideration towards the main challenges for FOWT projects: monitoring the system’s integrity, extending the lifespan of the components, and maintaining FOWTs safely at scale. Effectively and efficiently addressing these challenges would unlock the wide-scale deployment of FOWTs. In this work, we focus on one of the most critical components of the FOWTs, the Mooring Lines (MoLs), which are responsible for fixing the structure to the seabed. The primary mechanical failure mechanisms in MoLs are extreme load and fatigue, both of which are functions of the axial tension. An effective solution to detect long-term drifts in the mechanical response of the MoLs is to develop a Digital Twin (DT) able to accurately predict the behaviour of the healthy system to compare with the actual one. Moreover, we will develop another DT able to accurately predict the near future axial tension as an effective tool to improve the lifespan of the MoLs and the safety of FOWT maintenance operations. In fact, by changing the FOWT operational settings, according to the DT prediction, operators can increase the lifespan of the MoLs by reducing the stress and, additionally, in the case where FOWT operational maintenance is in progress, the prediction from the DT can serve as early safety warning to operators. Authors will leverage operational data collected from the world’s first commercial floating-wind farm [the Hywind Pilot Park (https://www.equinor.com/en/what-we-do/floating-wind/hywind-scotland.html.)] in 2018, to investigate the effectiveness of DTs for the prediction of the MoL axial tension for the two scenarios depicted above. The DTs will be developed using state-of-the-art data-driven methods, and results based on real operational data will support our proposal.

Industry 4.0 Digital Twins in Offshore Wind Farms

“…The historical data from sensors stored in AAS significantly contribute to predicting the wind speed (Hu et al, 2020;Li and Shen, 2022) and the future axial tension of mooring lines (Walker et al, 2021), and also to estimating the remaining useful life (RUL) of offshore wind farms (Mehlan et al, 2022). Branlard et al (2020b, a) presented digital twins of offshore wind farms in order to track the life cycle of the physical assets.…”

“…Here, the digital twin is a digital replica that collects and stores operating data based on deep learning algorithms from physical assets to illustrate how an Internet of things (IoT) works through its life cycle. Furthermore, Walker et al (2021) developed a digital twin of the mooring life tension using a state-of-the-art data-driven method to improve lifespan and safety. They designed the first digital twin to predict the behavior of the healthy system compared with the actual one, then subsequently constructed the second digital twin to forecast the future axial tension of the mooring line using existing data for safety purposes.…”

“…Walker et al (2021) applied (state-of-the-art) data-driven models (DDMs) as a digital twin framework to identify long-term drifts in the mechanical response of mooring lines for offshore wind turbines. The DDM technique utilizes the injection of configurator model components into the model dynamically, based on data received from external systems such as catalog systems.…”

State-of-the-Art Review of Vortex-Induced Motions of Floating Offshore Wind Turbine Structures

“…Mooring lines are one of the key elements of FOWTs. Digital twins were developed to detect the long-term drift and near-future axial tension of mooring lines to predict the extreme loads and fatigue [108]. The dynamic response of a fully non-linear model of a DeepCWind FOWT was investigated [109] after one of its three-catenary mooring systems was broken.…”

Data-driven forecasting of FOWT dynamics and load time series using lidar inflow measurements

“…Similarly, in [2], different network designs have been investigated for the prediction of blade root bending moments, wake center detection, and blade tip-tower clearance. In [3] data-driven digital twin models for the prediction and forecasting of mooring line tension based on measured operational time series data including floater dynamics signals as well as the ambient wind speed was developed. The use of lidar data for data driven fairlead tension time series and damage equivalent load predictions without forecasting has been been investigated in [4,5].…”