Floating Wind Turbine Structures Research Articles

Application of an active learning method for cumulative fatigue damage assessment of floating wind turbine mooring lines

Long-term cumulative fatigue damage of mooring lines is crucial for the design of floating wind turbine structures (FWTs). Although many efforts are carried out for the offshore floating platforms, there still needs to be an efficient approach for assessing the long-term fatigue damage of the floating wind turbine mooring lines due to the complex loading in FWTs. An active learning approach named AK-DA (Adaptive Kriging Damage Assessment) was recently proposed for the cumulative fatigue damage assessment of wind turbine structures. However, in the original work, the AK-DA approach was only tested on fatigue damage assessment of a 5MW wind turbine tower with monopile support structures. It is unclear whether it is applicable to other parts of the wind turbine system, especially considering the complex loading of FWTs. Therefore, in this work, the AK-DA approach is used to assess the cumulative fatigue damage of mooring lines. The Gaussian process regression (Kriging) model is used to predict the fatigue damage of the mooring line under different wind-wave cases. The cumulative fatigue damage of the mooring lines in the IEA 15MW semi-submersible wind turbines is assessed with the AK-DA approach. The numerical simulation results show that the AK-DA approach can efficiently and accurately estimate the cumulative fatigue damage of the mooring lines. Compared to the traditional simulation approach, the AK-DA approach can increase efficiency by more than 45 times, and the absolute error is less than 1%. This active learning approach could serve as a helpful tool for offshore mooring system designers, facilitating the cumulative fatigue damage assessment during the design process.

Comparison of Floating Offshore Wind Turbine Tower Deflection Mitigation Methods Using Nonlinear Optimal-Based Reduced-Stroke Tuned Vibration Absorber

Tower fatigue and strength are crucial operational concerns of floating offshore wind turbines (FOWTs) due to the escalation of the vibration phenomena observed on these structures as compared to land-based ones. FOWT towers are excited by wave and wind polyperiodic disturbances yielding continual transient states of structural vibration that are challenging for vibration mitigation systems. Thus, the paper investigates a novel implementation of nonlinear optimal-based vibration control solutions for the full-scale, tension leg platform (TLP)-based, NREL 5MW wind turbine tower-nacelle model with a 10-ton tuned vibration absorber (TVA), equipped with a magnetorheological (MR) damper, located at the nacelle. The structure is subjected to excessive wave and wind excitations, considering floating platform motions derived from model experiments in a wave tank. The MR damper operates simultaneously with an electromagnetic force actuator (forming a hybrid TVA) or independently (a semiactive TVA). The study includes both actuators’ nonlinearities and dynamics, whereby the former are embedded in the Hamilton-principle-based nonlinear control solutions. The TVA is tuned either to the NREL 5MW tower-nacelle 1st bending mode frequency (TVA-TN) or to the TLP surge frequency (TVA-TLP). The optimal control task was redeveloped concerning the TVA stroke and transient vibration minimisation, including the implementation of the protected structure’s acceleration and relative displacement terms, as well as the nonzero velocity term in the quality index. The regarded model is embedded in a MATLAB/Simulink environment. On the basis of the obtained results, the TVA-TN solution is by far superior to the TVA-TLP one. All the regarded TVA-TN solutions provide a tower deflection safety factor of ca. 2, while reference systems without any vibration reduction solutions or with a passive TVA-TLP are at risk of tower structural failure as well as the hybrid TVA-TLP system. The obtained TVA stroke reductions of 25.7%/22.0% coincide with 3.6%/10.3% maximum tower deflection reductions for the semiactive/hybrid TVA-TN case (respectively) with regard to the previously developed approaches. Moreover, these reductions are obtained due to the sole control algorithm enhancement; thus, no additional resources are necessary, while this attainment is accompanied by a reduction in the required MR damper force. The lowest obtained TVA stroke amplitude of 1.66 m is guaranteed by the newly introduced semiactive control. Its hybrid equivalent ensures 8% lower primary structure deflection amplitude and reduced nacelle acceleration levels thanks to the utilisation of the force actuator of the relatively low power (ca. 6 kW); the trade-off is an increased TVA stroke amplitude of 2.19 m, which, however, is the lowest among all the tested hybrid solutions. The analysed reference passive TVA systems, along with a modified ground-hook hybrid solution, can hardly be implemented in the nacelle (especially along the demanding side–side direction). The latter, being the well-proven hybrid solution for steady-state tower deflection minimisation, yielded unsatisfactory results. The achievements of the study may be used for an effective design of a full-scale vibration reduction system for the TLP-based floating wind turbine structure.

A digital twin-based framework for damage detection of a floating wind turbine structure under various loading conditions based on deep learning approach

Engineering has many necessary fields, and Structural Health Monitoring (SHM) is one of the most important of them. Sometimes in industrial environments, it is difficult and even impossible to collect data containing different real damages. Therefore, the problem of data acquisition represents a primary challenge in designing damage detection systems. The application of digital twin methods based on simulated models and/or Machine Learning (ML) models is a practical way to solve this problem. In this approach, a digital twin is generated for a compromised structure, utilizing a physics-based model to analyze diverse damage scenarios. Subsequently, an ML model is trained using data extracted from the physics-based model, functioning as the digital twin. This research proposes a method based on a digital twin for detecting damages in structures. The data produced from a Floating Wind Turbine (FWT) model was used to evaluate the performance of the proposed digital twin-based method. For this purpose, the FWT structure was simulated using a numerical model to address the data collection problem in the face of various uncertainties, such as changing loading conditions. In line with the concept of digital twin and to reduce the computational time, a Deep Convolution Long Short-Term Memory Neural Network (DCLSTMNN) model was designed and trained only with the frequency data of various scenarios of the simulated FWT model under constant loads (deterministic loads, including constant wind speed and airy wave model) to learn the damage-sensitive features. Then, to demonstrate the robustness of the proposed model under different uncertainties, the DCLSTMNN model was evaluated using the frequency data of the simulated FWT model under variable loading conditions (including Kaimal wind model and JONSWAP wave theory). Some vibration response components unrelated to the nature of the FWT model were removed using the Complete Ensemble Empirical Mode Decomposition (CEEMD) method. Then, the reconstructed vibration responses were used to create the frequency data using the Frequency Domain Decomposition (FDD) technique. The study results show that the proposed digital twin-based method can detect the location and severity of damage more accurately than other comparable methods despite various uncertainties.

A digital twin-based framework for simulation and monitoring analysis of floating wind turbine structures

It is currently a prevailing trend to adopt clean energies instead of traditional ones due to the global climate change caused by carbon emissions. Offshore wind farms, in particular, have emerged as a crucial source of renewable energy, owing to their benefits such as no land occupation and abundant resources. However, the design, installation, operation, and maintenance (O&M) of floating wind turbines (FWTs) involve multiple sources of heterogeneous data, which pose challenges to data integration and management, as well as to the simulation and analysis of FWTs. To address this issue, this study proposes a unified framework based on digital twin (DT) to acquire and integrate diverse types of information used throughout the entire life cycle of FWTs. A digital 3D model serves as a medium to enable real-time synchronization and inversion of sensor data, facilitating the simulation and analysis of the global state of FWTs. The proposed framework is evaluated through a case study of a twin-barge float-over project, which includes process simulation, mechanical analysis, and anomaly identification. The results demonstrate that DT can facilitate timely monitoring and analysis of FWTs, and enable visualization of construction plans, early warning of structural abnormalities, and accurate recognition of FWT posture and marine environment. The case study validates the efficacy of the proposed framework in ensuring personnel and equipment safety, optimizing project plans, and improving construction efficiency.

Stability analysis of floating wind turbine structure with turned mass damper-nonlinear energy sink

The conventional tuned mass damper (TMD) has the defect of a narrow tuning band in the stability control of a floating wind turbine (FWT). In this paper, a hybrid vibration reduction method of tuned mass damper-nonlinear energy sink (TMD-NES) is proposed, which makes full use of the broadband advantage of NES and can effectively avoid its initial energy sensitivity disadvantage. A FWT dynamics model with TMD-NES was established, the complex variable average method was used to solve the dynamic response of the system, and the Runge–Kutta method is used to prove the reliability of the analytical method. The bifurcation characteristics of NES and the vibration suppression effect of TMD-NES are analyzed and discussed. The results show that the proposed method reduces the peak energy of the tower surge response by 95.3%, broadens the frequency band of vibration reduction, reduces the sensitivity to the initial energy, and improves the robustness of the system.

Parent Nested Optimizing Structure for Vibration Reduction in Floating Wind Turbine Structures

A tuned mass damper (TMD) is a system that effectively reduces the vibrations of floating offshore wind turbines (FOWTs). To maximize the performance of TMDs, it is necessary to optimize their design parameters (i.e., stiffness, damping, and installation location). However, this optimization process is challenging because of the existence of multiple local minima. Although various methods have been proposed to determine the global minimum (e.g., exhaustive search, genetic algorithms, and artificial fish swarm algorithms), they are computationally intensive. To address this issue, a novel optimization approach based on a parent nested optimizing structure and approximative search is proposed in this paper. The approximative search determines an initial parameter set (close to the optimal set) with fewer calculations. Then, the global minimum can be rapidly determined using the nested and parent optimizers. The effectiveness of this approach was verified with an FOWT exposed to stochastic winds. The results show that this approach is 30–55 times faster than a conventional global optimization method.

Substantial Research Secures the Blue Future for our Blue Plant

Substantial Research Secures the Blue Future for our Blue Plant

Damage identification of a TLP floating wind turbine by meta-heuristic algorithms

Damage identification of the offshore floating wind turbine by vibration/dynamic signals is one of the important and new research fields in the Structural Health Monitoring (SHM). In this paper a new damage identification method is proposed based on meta-heuristic algorithms using the dynamic response of the TLP (Tension-Leg Platform) floating wind turbine structure. The Genetic Algorithms (GA), Artificial Immune System (AIS), Particle Swarm Optimization (PSO), and Artificial Bee Colony (ABC) are chosen for minimizing the object function, defined properly for damage identification purpose. In addition to studying the capability of mentioned algorithms in correctly identifying the damage, the effect of the response type on the results of identification is studied. Also, the results of proposed damage identification are investigated with considering possible uncertainties of the structure. Finally, for evaluating the proposed method in real condition, a 1/100 scaled experimental setup of TLP Floating Wind Turbine (TLPFWT) is provided in a laboratory scale and the proposed damage identification method is applied to the scaled turbine.

Coupled wind-wave time domain analysis of floating offshore wind turbine based on Computational Fluid Dynamics method

As offshore wind farms are extending into the deep sea around the world, the coupled aerodynamic and hydrodynamic time domain analysis of floating offshore wind turbine is of both profound academic interests and important practical interests. In this work, one typical tension leg platform of 5 MW wind turbine system has been chosen to the objective. The aerodynamic loads of the NREL 5 MW offshore wind turbine have been simulated based on Navier-Stokes equations and renormalization group (RNG) k-ε turbulence mode, which is suitable for the analysis of rotational flow field near wind turbine blades. The hydrodynamic loads of the floating wind turbine platform have been simulated by viscous numerical flume based on volume-of-fluid method. The dynamic responses of the floating wind turbine system could be obtained by solving the dynamic equations of the floating system under typical coupled wind-wave sea cases, and the motion of the floating platform under different time steps would be simulated by advanced sliding mesh technique. As a result, the fluid-structure interactions between the floating wind turbine structure and the coupled wind-wave flow field have been successfully simulated. Furthermore, the availability of the coupled numerical method has been verified by comparing the numerical results with corresponding scale model test data.