bluuebunny/crossref_metadata_2025_split huggingface dataset:
Performance assessment of wake mitigation strategies
Authors: Coquelet M , Moens M , Bricteux L , Crismer J-B , Chatelain P 
Published: June 2022

Abstract
Many control strategies have emerged in the field of wind energy to address wake interaction effects and try to maximize wind farm power production. They rely either on wake redirection or on induction control. While wake redirection is easily performed by yawing the turbine, efficient and practically deployable induction control is not as straightforward. Recent studies paved the way with periodic dynamic induction control (PDIC), a strategy that generates pulsatile patterns in the wake and increases wake remixing. This study aims at further investigating the physics behind PDIC through large eddy simulations at high resolution. Results show the wake shear layer destabilization leading to the pulsatile wake. This paper also presents a comprehensive comparison between greedy control, PDIC and static yaw. It focuses both on power and loads at the scale of a pair of in-line turbines. It shows that PDIC significantly increases fatigue loads and leads to moderate power gains, while properly oriented yaw leads to fatigue load reduction on the upstream turbine and noticeable increase of the power production.
Design of gain-scheduling cascade control for a motion compensated gripper frame
Authors: Schepers J M , Fidalgo Domingos D A , ter Braak J , van Winsen I , van Wingerden J W 
Published: June 2022

Abstract
The offshore wind market is growing, resulting in larger wind turbines being installed farther away from the coast and into deeper waters. This trend brings challenges to an industry strongly depending on the use of jack-up vessels. A floating vessel equipped with a Dynamic Positioning (DP) system and a Motion Compensated Gripper Frame (MCGF) provides a more efficient solution to guide XXL monopoles during the installation phase. According to strict regulations, the offset angle of monopiles shall not be over 0.25°. To ensure this, a robust control method is required to deal with gradual change in soil stifness, wave induced motions and sensor delays. The control force exerted by the gripper frame can significantly affect the floating vessel dynamics, hence, this should be optimized.
Therefore, the research goal is to analyse the system and design a robustly stable control for the MCGF. In the context of this research, cascade PID control with gain-scheduling is chosen, which is able to control the Gripper Frame during all installation steps for different soil stiffness and to reject wave disturbances.
With the use of the lumped multiplicative uncertainty structure, the changing and uncertain soil-characteristics are modelled. The sensor delay to measure the monopile inclination, has a significant impact on system stability. Robust stability criteria are validated using the Nyquist criterion and by analysing the system’s closed-loop poles. With the use of gain-scheduling it is possible to switch control settings during the installation. Time-domain results show that with this design approach the installation criteria are met such as the maximum monopile angle and the applied control force.
Ultimately, it is shown that robust PID control with gain-scheduling for a MCGF can ensure safe, efficient and cost-effective installation of the next generation wind turbines. Future research should focus into the definition and implementation of the gain-scheduling switching criteria.
Detection of Jacket Offshore Wind Turbine Structural Damage using an 1D-Convolutional Neural Network with a Support Vector Machine Layer
Authors: Tutivén Christian , Moreno Sueanny , Vidal Yolanda , Benalcázar Carlos 
Published: June 2022

Abstract
Because offshore wind turbines, particularly their foundations, operate in hostile environments, implementing a structural health monitoring system is one of the best ways to monitor their condition, schedule maintenance, and predict possible fatal failures at lower costs. A novel strategy for detecting damage in offshore wind turbine jacket foundations is developed in this work, based on a vibration monitoring methodology that reshapes the data into a multichannel array, with as many channels as correlated sensors with the predicted variable, a 1-D deep convolutional neural network to extract temporal features from the monitored data, and a support vector machine as a final classification layer. The obtained model allows the detection of three types of bar states: healthy bar, cracked bar, and bar with an unlocked bolt.
Identifying evolving leading edge erosion by tracking clusters of lift coefficients
Authors: Abdallah Imad , Duthé Gregory , Barber Sarah , Chatzi Eleni 
Published: June 2022

Abstract
This work proposes an approach to identify Leading Edge Erosion (LEE) of a wind turbine blade by tracking evolving and emerging clusters of lift coefficients CL
time-series signals under uncertain inflow conditions. Most diagnostic techniques today rely on direct visual inspection, image processing, and statistical analysis, e.g. data mining or regression on SCADA output signals. We claim that probabilistic multivariate spatio-temporal techniques could play an eminent role in the diagnostics of LEE specifically leveraging CL
time-series signals form multiple sections along the span of the blade. The proposed method extracts clusters’ features based on Variational Bayesian Gaussian Mixture Models (VBGMM) and tracks their spatial and temporal changes, as well as interpret the evolution of the clusters through prior physics-based assumptions. The parameters of the VBGMM are the mean, the eigenvalues and eigenvectors of the covariance matrix, and the angle of orientation of the eigenvectors. We show that the distribution of the CL
data may not show statistically separable clusters, however, the parameters of the VBGMM clusters fitted to the CL
data, allows to discriminate moving clusters primarily due to varying inflow and operating conditions, versus emerging clusters primarily due to evolving severity of the blade LEE.
Simplified dynamic inflow for control engineering models
Authors: Geisler Jens 
Published: June 2022

AbstractSimplified turbine models are needed wherever computational resources are limited and the model needs to run in real-time or faster, or when it is used in analytical procedures such as linearization. These use cases are typically given in control engineering applications, especially advanced methods such as nonlinear model predictive control or state estimation. Many such models with varying levels of detail have been proposed, but most of them rely on a quasi-stationary, rotor averaged/effective representation of aerodynamic forces. Unsteady aerodynamic effects such as dynamic inflow (sometimes referred to as dynamic wake) or dynamic stall are usually neglected. But with ever more advanced methods more detailed yet still computationally efficient models are needed. Therefore, this work proposes an approach for adding the effect of dynamic inflow to any rotor effective force or moment that was previously modeled as quasi-stationary. Novel to this work is the suitability for nonlinear as well as linear models and a focus on computational efficiency while still optimally recreating the effect.
SCADA Data-Driven Wind Turbine Main Bearing Fault Prognosis Based on Principal Component Analysis
Authors: Campoverde Lorena , Tutivén Christian , Vidal Yolanda , Benaláazar-Parra Carlos 
Published: June 2022

Abstract
Condition monitoring for wind turbines is essential for the further development of wind farms. Currently, many of the works are focused on the installation of new sensors to predict turbine failures, which raises the cost of wind projects. Wind turbines operate in a wide variety of environmental conditions, such as different temperatures and wind speeds that vary throughout the year season. Typically, most or all of the data available in a turbine is healthy data (operation without failure), so data-driven supervised classification methods have data imbalance problems (more data from one class). Also, when historical pre-failure data do not exist, those methods cannot be used. Taking into account the aforementioned difficulties, the stated strategy in this work is based on a principal component analysis anomaly detector for main bearing failure prognosis and its contributions are: i) this methodology is based only on healthy SCADA data, ii) it works under different seasons of the year providing its usefulness, iii) it is based only on external variables and one temperature related to the element under diagnosis, thus avoiding data containing information from other fault types, iv) it accomplishes the main bearing failure prognosis (several months beforehand), and v) the performance of the proposed strategy is validated on a real in production wind turbine.
Wind turbine load estimation using machine learning and transfer learning
Authors: Xu Guanqun , Yu Wei , Kim Taeseong 
Published: June 2022

Abstract
Machine learning method has always been popular to solve wind turbine related problems at a data level. However, with the limitation of the availability of relevant data, transfer learning has gained increasing attention. In this study, traditional machine learning method of artificial neural networks (ANN), together with parameter-based transfer learning method has been used to estimate wind turbine load. First, ANN load model was built for DTU 10MW wind turbine as well as NREL 5MW wind turbine. Then, parameter-based transfer learning has been applied to the above-mentioned models to estimate load for a different turbine type or two mixed turbine types. Results indicate that ANN method provides good estimation on wind turbine fatigue load. For DTU 10MW ANN model, the trend of accuracy becomes steady as the number of input samples increases and 1500 samples is deemed as the optimal number of samples for training DTU 10MW. In addition, with transfer learning, it was succeeded in building NREL 5MW model with corresponding DTU 10MW pretrained model but failed in establishing mixed dataset model neither with DTU 10MW nor with NREL 5MW pretrained model.
Wind turbine generator prognostics using field SCADA data
Authors: Peter Rudolph , Zappalá Donatella , Schamboeck Verena , Watson Simon J 
Published: June 2022

Abstract
This paper presents a novel prognostic method to estimate the remaining useful life (RUL) of generators using the SCADA (Supervisory Control And Data Acquisition) systems installed in wind turbines. A data-driven wind turbine anomaly classification method is developed. The anomalies are quantified into a health indicator to measure the component degradation over time. An Autoregressive Integrated Moving Average (ARIMA) time series forecasting technique is then applied to predict the RUL of the wind turbine generator. The proposed method has been validated using industry field data showing accurate predictions of RUL with a 21 day lead time for maintenance of the turbine.
Experimental Analysis of the Wake Meandering of a Floating Wind Turbine under Imposed Surge Motion
Authors: Pardo Garcia L. , Conan B. , Aubrun S. , Perret L. , Piquet T. , Raibaudo C. , Schliffke B. 
Published: June 2022

Abstract
With the development of floating wind farms, the understanding of the far wake becomes of utmost importance because it cannot only be based on the experience acquired on fixed offshore turbine as the range of motion of the floater due to met-ocean conditions is in the same order of magnitude compared to the energetic turbulent scales of the atmosphere and to the characteristic scales of the wake. The objective of this wind-tunnel experiment is to analyse the behaviour of the wake center of a floating turbine subject to imposed surge motion. The experiment is carried out in the LHEEA wind tunnel where a 1/500 scale wind turbine model is immersed in a realistic offshore atmospheric-boundary layer. The model is actuated in surge by a linear motor able to reproduced idealised second order surge motion of a floating platform (mainly due to the waves) with a realistic amplitude and range of frequencies. Stereoscopic particle image velocimetry measurements are performed at two planes normal to the free-stream at distances of 4.6 and 8.1 diameters downstream of the turbine. Instantaneous velocity fields acquired at 14.1 Hz are individually analysed by convolution to find the location of the wake center. Results show that the wake center spreading is largely influenced by the downstream distance and only slightly affected by the surge motion. However, when analysing the wake position time series by a power spectral density, the signature of the surge motion becomes very clear for both downstream distances. These findings tell that the far wake (at least up to 8.1 diameters downstream) of a floating wind turbine has a “memory” of the motion in its frequency content. When extrapolating to a floating wind farm, this result suggests that a turbine inside a floating wind farm will be immersed in a flow including a significant dynamic signature in the range of its own motion.
A parametric study of the mooring system design parameters to reduce wake losses in a floating wind farm
Authors: Youssef Mahfouz Mohammad , Hall Matthew , Cheng Po Wen 
Published: June 2022

Abstract
Wake effects inside a conventional fixed bottom wind farm decrease the power produced by the downwind turbines, hence decreasing the farm’s annual energy production (AEP). However, floating offshore wind turbines (FOWTs) have the ability to relocate their positions laterally through surge and sway motions. This flexibility provides a new degree of freedom (DOF) in the floating wind farm layout, which can be used to decrease the aerodynamic interactions inside the floating wind farm and hence decrease the wake losses. The lateral movement of FOWTs can be passively controlled by the mooring system design. The mooring system’s restoring characteristics allows the FOWT to only move within a specific area in the x-y plane known as the watch circle. Current state of the art mooring system designs are following oil and gas design basis where the floating platforms are not allowed to have large lateral displacements. In this work, we use full factorial design to analyse the effect of different mooring system design parameters on the ability of the floater to relocate its position. The analysis shows that each design parameter has a different way of affecting the FOWT’s response. The mooring lines’ headings control which wind directions cause the biggest displacements in the crosswind direction. The smaller the lines’ diameters the higher the displacements of the FOWT. Finally, the longer the line length the smaller the mooring system’s stiffness and hence the larger the FOWT’s displacement. The results of this study can be used as the basis for floating wind farm optimization, in which the wind turbines are allowed to passively relocate their positions according to the wind speed and wind direction.
Experimental validation of the aero-servo design of a large-scale floating offshore wind turbine model
Authors: Taruffi Federico , Di Carlo Simone , Muggiasca Sara , Belloli Marco 
Published: June 2022

Abstract
Offshore floating wind turbines are such complex systems from the point of view of global dynamics that numerical models are often insufficient in describing completely and correctly their behavior. In this framework large scale models deployed in a natural outdoor environment are a valid complement in understanding the real features of the system and in updating or validating codes. In the case of wind turbines adopting a large scale allows to better reproduce the aerodynamic behavior of the rotor. Objective of the present work is the validation of the aerodynamic design of a large-scale 1:15 model of the DTU 10 MW wind turbine deployed on a multipurpose floating platform, built in the framework of the Horizon 2020 project termed as “The Blue Growth Farm Project”. It is requested to assess the effective aerodynamic characteristics of the rotor so to understand if the physical design is well respecting the aerodynamic properties requested in the design phase. Particular care is put in the correct reproduction of the operating parameters as part of the assessment of the control system. For initial analysis a good compliance between design and physical model is found. Regarding the aerodynamic characteristics, a good agreement between the experimental Cp values and the numerical curves is observed. Overall, the physical rotor aerodynamic design is validated and the power controller steady-state performances are assessed.
Multi-fidelity Analyses of Rotor Loads of Floating Offshore Wind Turbines with Wind/Wave Misalignment
Authors: Ortolani A. , Papi F. , Bianchini A. , Persico G. , Drofelnik J. , Campobasso M.S. 
Published: June 2022

Abstract
Reliable predictions of the aero- and hydrodynamic loads of fixed-bottom and floating offshore wind turbines are paramount for assessing fatigue life and designing load and power control systems. However, significant uncertainty affecting aerodynamic predictions still exists. This study presents cross-comparative analyses of the predictions of aerodynamic loads and power of fixed-foundation and floating wind turbine rotors with and without yaw errors using time- and frequency-domain Navier-Stokes Computational Fluid Dynamics, and the Blade Element Momentum theory. The considered test case is the National Renewable Energy Laboratory 5 MW reference turbine, assumed to be mounted in the floating case on a semi-submersible platform and undergoing pitching motion about the tower base. Although the overall qualitative agreement of the low- and high-fidelity predictions is found to be fair in all cases, for the considered regimes the agreement between the two methods is better for the pitching rotor in aligned wind than for the yawed flows regardless of the tower motion.
Development of a Genetic Algorithm Code for the Design of Cylindrical Buoyancy Bodies for Floating Offshore Wind Turbine Substructures
Authors: Benifla Victor , Adam Frank 
Published: June 2022

Abstract
The Levelized Cost of Energy for floating offshore wind must decrease significantly to be competitive with fixed offshore wind projects or even with onshore wind projects. This study focuses on the design optimization of cylindrical buoyancy bodies for Floating Offshore Wind Turbine substructures. The presented work is based on a previously studied buoyancy body design that allows an efficient manufacturing process and integration into different substructures. In this study, an optimization framework using a particular Genetic Algorithm is developed to parameterize the cylindrical geometry of the buoyancy body and optimize its design in terms of cost, considering loads acting on the structure as well as manufacturing and floater specific dimension restrictions. In this paper, the specific characteristics of this particular buoyancy body are highlighted and the implementation of the optimization process is detailed. Preliminary results for a given study case are presented and discussed before pointing out potential cost reductions and future improvements to be made with regards to the presented optimization work.
A Flexible, Multi-fidelity Levelised Cost of Energy Model for Floating Offshore Wind Turbines Multidisciplinary Design, Analysis and Optimisation Approaches
Authors: Sykes V , Collu M , Coraddu A 
Published: June 2022

Abstract
As the UK takes a step towards a greener, cleaner future aiming to be net zero by 2050, continuous development of the power network is required. A clear solution is offshore wind, having already proved its feasibility and success in nearshore sites. However, a large majority of near shore sites in the UK are already being utilised. The next step is to move into deeper waters and utilise the stronger, more consistent wind resources. A solution could be floating offshore wind which is still in its infancy, with only a few operational floating wind farms installed. Building upon the multidisciplinary design, analysis, and optimisation framework (MDAO) for floating offshore wind turbines (FOWT) being developed at the University of Strathclyde, called FEDORA, the aim of this work is to refine the LCoE model adopted by FEDORA, and applying it to perform the optimisation of the floating offshore OC3 SPAR. There is limited data on cost, therefore Hywind Scotland Pilot Park will be used as a basis for the LCoE model, allowing the results to be validated. This model is not restricted to SPARs, as it establishes a general methodology to calculate the life cycle cost of floating offshore wind farms. Utilising the improved cost model this work finds four optimised SPAR structures for four different maximum angles of inclination which can be experienced in the wind turbines operation. The improved cost model has a much higher accuracy, highlighting the initial cost model underestimates the cost of the SPAR structure by around half.
Updates on the OpenFAST Lidar Simulator
Authors: Guo Feng , Schlipf David , Zhu Hailong , Platt Andy , Cheng Po Wen , Thomas Florian 
Published: June 2022

Abstract
Lidar systems are able to measure the wind speed remotely by detecting the aerosol movement caused by wind. A nacelle-based lidar system scanning the wind in front of a wind turbine can provide a preview of the incoming wind before the wind interacts with the turbine. Implementing a realistic lidar simulator into the wind turbine aero-elastic simulation tool can be beneficial for various wind energy related fields, such as lidar-assisted control, load validation, and load monitoring. In previous work, a lidar simulation module has been integrated into the open-source aero-elastic simulation tool OpenFAST, covering already lidar characteristics like different scan patterns, the volume averaging along the beam, and the coupling with the nacelle motion due to turbine tower dynamics. This paper focuses on adding further features to the lidar simulation module of OpenFAST to make the lidar simulation more realistic: the evolving turbulence, the blade blockage effect, and the adjustable data availability. Further, the wind preview quality with the new features is assessed.
Fatigue study of an inverse-designed low induction rotor using open-source tools
Authors: Major D , Churchfield M , Schmitz S 
Published: June 2022

Abstract
The current trend in wind turbine design leans toward increasing rotor diameter for increased energy capture. This presents a notable fatigue concern as blade loads increase with blade length. One solution to potentially alleviate the additional blade loading and improve the fatigue behavior of large-diameter wind turbines, while also reducing overall cost of energy, is the low-induction rotor. The purpose of this work is to perform a holistic analysis to assess the attainable benefits of a notional low-induction rotor variant of the DTU 10MW Reference Wind Turbine using multi-disciplinary open-source design and analysis tools. The notional low-induction rotor blade is designed using an inverse-design routine that allows for the direct specification of desired blade properties. To assess viability of the new rotor design, annual energy production and levelized cost of energy are considered along with fatigue at the blade root and the associated drivetrain and tower response to changes in blade loading. It is estimated that the proposed early-concept low-induction rotor design improves annual energy production by 6.35% (or 3 GW-h), resulting in a 4% (0.25 ¢/kW-h) reduction in levelized cost of energy. The low-thrust nature of the low-induction rotor design reduces root flap bending moment fatigue by up to 21.5%, and it is proposed that changes in blade root edgewise fatigue for the longer, heavier blades of the low-induction rotor can be alleviated with mass redistribution.