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Dynamic response analysis of floating wind turbine platform in local fatigue of mooring
... Wind turbine technologies have been drawing more and more attentions due to its maturity and sustainability, and the role of renewable energy [1]. In particular, offshore wind turbines (OWTs) have been vigorously developed to take advantage of higher wind capturing efficiency, less land occupancy and lower noise pollutions at the offshore wind field [2]. Comparing with their onshore counterparts, besides the aerodynamic loadings from the stochastic wind actions, the OWT structure is also subjected to hydrodynamic loadings from the stochastic wave actions at its service environment [3]. ...
... x n = q 7 + q 9 + Hq 10 y n = q 8 + q 11 + Hq 12 (2) where x n and y n are horizontal displacements of nacelle at F-A and S-S directions. ...
In view of an especial frequency tuning pattern, a prestressed tuned mass damper (PSTMD) with a larger mode damping has been proposed and explored its vibration control competence for offshore wind turbine (OWT). Unlike the vibration control research under a certainly given wind-wave actions, the fatigue damage of OWT structure is directly associated with the stochastic and complex environment correlation variables in real offshore site. This paper further develops the bi-directional PSTMD to mitigate the fatigue damage of OWT structure subjected to stochastic wind-wave actions. The mathematical models of OWT and bi-directional PSTMD are established to deduce the fatigue stress, and the aerodynamic and hydrodynamic loads from stochastic wind-wave actions. Some met-ocean data are used to study the stochastic probability distribution, and build the joint probabilistic density functions from various environmental factors. The Rain-flow Counting, S-N curve and Miner's rule algorithms are used to comparatively explore the fatigue damage mitigation of bi-directional PSTMD under the stochastic wind-wave actions. The results show that this bi-directional PSTMD can mitigate the fatigue damage over 53.73% and 54.95% compared with the conventional pendulum TMD (PTMD) at the tower-base and seabed cross sections respectively.
... Sun et al. tackled the fatigue issues in floating offshore wind turbine moorings due to prolonged exposure to wind, waves, and currents [133]. They introduced a CNNt-distribution Stochastic Neighbor Embedding model to automatically detect damage severity in these systems. ...
... Figure 15. The architecture of the CNN used by [133] as part of their framework for damage detection in floating offshore wind turbine mooring systems. Table 3 provides a list of the literature covered in this section, along with the corresponding ML techniques employed and concise summaries of the research conducted. ...
The continuous advancement within the offshore wind energy industry is propelled by the imperatives of renewable energy generation, climate change policies, and the zero-emission targets established by governments and communities. Increasing the dimensions of offshore wind turbines to augment energy production, enhancing the power generation efficiency of existing systems, mitigating the environmental impacts of these installations, venturing into deeper waters for turbine deployment in regions with optimal wind conditions, and the drive to develop floating offshore turbines stand out as significant challenges in the domains of development, installation, operation, and maintenance of these systems. This work specifically centers on providing a comprehensive review of the research undertaken to tackle several of these challenges using machine learning and artificial intelligence. These machine learning-based techniques have been effectively applied to structural health monitoring and maintenance, facilitating the more accurate identification of potential failures and enabling the implementation of precision maintenance strategies. Furthermore, machine learning has played a pivotal role in optimizing wind farm layouts, improving power production forecasting, and mitigating wake effects, thereby leading to heightened energy generation efficiency. Additionally, the integration of machine learning-driven control systems has showcased considerable potential for enhancing the operational strategies of offshore wind farms, thereby augmenting their overall performance and energy output. Climatic data prediction and environmental studies have also benefited from the predictive capabilities of machine learning, resulting in the optimization of power generation and the comprehensive assessment of environmental impacts. The scope of this review primarily includes published articles spanning from 2005 to March 2023.
... The electromechanical-rigid-flexible coupling dynamic model improves the stability and safety of the system, particularly under gust conditions [33]. Through analyzing the dynamics of FWT platform mooring from structure creep to failure, it was found that the yaw response is the most sensitive to structural damage [34]. The shared anchoring system applied to offshore floating wind turbines further reduces the cost of wind turbines by reducing the cost of manufacturing and installation [35]. ...
... This is equivalent to the Kutta condition of unsteady flow. When the vortex is separated, its strength remains unchanged (Kelvin's law) and does not carry aerodynamic load, so it moves with local speed [34]. The wind velocities in the rotor plane are depicted in Figure 12. ...
Dynamic response of flexible multi-body large wind turbines has been quickly growing in recent years. With the new normal economic policy, the economy of China is developing innovatively and stably. New energy development and utilization is an important strategy for people’s lives and economic development around the world. It is feasible to analyze from a broad perspective. In particular, the development and application of wind power is affecting the economic development of industry to a certain extent. Persistent and significant large wind turbines have cast concern over the prospects of wind power technology, and a comprehensive development potential of wind power technology has been analyzed its potential use in the future. The multi-body dynamics method can better analyze and describe the impact of flexible blade elastic deformation on motion characteristics and provides a practical analysis method for the aeroelastic stability analysis and control system design of wind turbines.
... For example, considerable efforts have been devoted to understanding their structural response and motion behavior. However, existing studies predominantly focus on analyses under simplified conditions, and in-depth studies on dynamic responses under complex marine conditions (e.g., combining waves, wind, and ocean currents) and real environments, especially extreme environments, are lacking [2]. China is one of the world's high-incidence countries for typhoons, with several typhoons along the coast each summer, causing significant impacts and losses to wind farms. ...
The accurate prediction of short-term platform motions in a real environment is crucial for the safe design, operation, and maintenance of floating offshore wind turbines (FOWTs). Numerical simulations of motions are typically associated with high uncertainties due to abstracted theoretical models, empirical parameters, initial environment parameters, etc. Therefore, it is necessary to integrate other sources of information associated with less uncertainty, e.g., monitoring data, for accurate predictions. In this paper, we propose a probabilistic prediction based on the Bayesian approach that logically integrates motion monitoring data with simulated motion predictions of FOWTs, considering uncertainties in the environment model, structural properties, motion prediction method, monitoring data, etc. The approach consists of constructing a prior probability density function (PDF) of a random variable (which characterizes the largest value of the initial motion response) via numerical simulations and a likelihood function based on platform motion monitoring data and deriving a posterior PDF of the random variable by Bayesian updating. Then, posterior distributions of short-term extreme motion responses are derived using the posterior PDF of the random variable, representing lower uncertainty and improved accuracy. A Metropolis-Hastings algorithm is adopted to obtain PDFs of complex probability distributions. The effectiveness of the approach is demonstrated on a real FOWT platform in Scotland. The proposed probabilistic prediction approach results in posterior distributions of short-term extreme platform motions associated with less uncertainty and higher accuracy, which is attributed to integrating prior knowledge with monitoring data.
... For example, considerable efforts have been devoted to understanding their structural response and motion behavior. However, existing studies predominantly focus on analyses under simplified conditions, and in-depth studies on dynamic responses under complex marine conditions (e.g., combining waves, wind, and ocean currents) and real environments, especially extreme environments, are lacking [2]. China is one of the world's high-incidence countries for typhoons, with several typhoons along the coast each summer, causing significant impacts and losses to wind farms. ...
The accurate prediction of short-term platform motions in a real environment is crucial for the safe design, operation, and maintenance of floating offshore wind turbines (FOWTs). Numerical simulations of motions are typically associated with high uncertainties due to abstracted theoretical models, empirical parameters, initial environment parameters, etc. Therefore, it is necessary to integrate other sources of information associated with less uncertainty, e.g., monitoring data, for accurate predictions. In this paper, we propose a probabilistic prediction based on the Bayesian approach that logically integrates motion monitoring data with simulated motion predictions of FOWTs, considering uncertainties in the environment model, structural properties, motion prediction method, monitoring data, etc. The approach consists of constructing a prior probability density function (PDF) of a random variable (which characterizes the largest value of the initial motion response) via numerical simulations and a likelihood function based on platform motion monitoring data and deriving a posterior PDF of the random variable by Bayesian updating. Then, posterior distributions of short-term extreme motion responses are derived using the posterior PDF of the random variable, representing lower uncertainty and improved accuracy. A Metropolis-Hastings algorithm is adopted to obtain PDFs of complex probability distributions. The effectiveness of the approach is demonstrated on a real FOWT platform in Scotland. The proposed probabilistic prediction approach results in posterior distributions of short-term extreme platform motions associated with less uncertainty and higher accuracy, which is attributed to integrating prior knowledge with monitoring data.
... Moreover, for modern large-scale and floating wind turbines, the integration of artificial intelligence methods with key structural fatigue load modeling and optimization is particularly important. For instance, a CNN-t-SNE-based neural network model for structural fatigue analysis of floating wind turbine platforms is developed [33], enabling automatic detection of damage in mooring equipment. Further, a control network model based on multiagent theory has been proposed to assess fatigue loads in offshore wind turbines [34]. ...
As global energy crises and climate change intensify, offshore wind energy, as a renewable energy source, is given more attention globally. The wind power generation system is fundamental in harnessing offshore wind energy, where the control and design significantly influence the power production performance and the production cost. As the scale of the wind power generation system expands, traditional methods are time-consuming and struggle to keep pace with the rapid development in wind power generation systems. In recent years, artificial intelligence technology has significantly increased in the research field of control and design of offshore wind power systems. In this paper, 135 highly relevant publications from mainstream databases are reviewed and systematically analyzed. On this basis, control problems for offshore wind power systems focus on wind turbine control and wind farm wake control, and design problems focus on wind turbine selection, layout optimization, and collection system design. For each field, the application of artificial intelligence technologies such as fuzzy logic, heuristic algorithms, deep learning, and reinforcement learning is comprehensively analyzed from the perspective of performing optimization. Finally, this report summarizes the status of current development in artificial intelligence technology concerning the control and design research of offshore wind power systems, and proposes potential future research trends and opportunities.
Offshore support structures are critical for offshore bottom-fixed wind turbines, as they bear nearly all the mass and loading of wind turbine systems. In addition, the support structures are generally subjected to a harsh environment and require a design life of more than 20 years. However, the design validation of the support structure normally needs thousands of simulations, especially considering the fatigue limit state. Each simulation is quite time-consuming. This makes the design optimization of wind turbine support structures lengthy. Therefore, an effective approach for estimating the fatigue damage of wind turbine support structures is essential. This work uses a machine learning method named the AK-DA approach for cumulative fatigue damage of wind turbine support structures. An offshore site in the Atlantic Sea is studied, and the related joint probability distribution of wind-wave occurrences is adopted in this work. The IEA 15MW wind turbine with monopile support structure is investigated, and different wind-wave conditions are considered. The cumulative fatigue damage of the monopile support structure is estimated by the AK-DA approach. The numerical results showed that this machine learning approach can efficiently and accurately estimate the cumulative fatigue damage of the monopile support structure. The efficiency is increased more than 55 times with an error of around 1%. The AK-DA approach can highly enhance the design efficiency of offshore wind support structures.
Floating offshore wind has received more attention due to its advantage of access to incredible wind resources over deep waters. Modeling of floating offshore wind farms is essential to evaluate their impacts on the electric power system, in which the floating offshore wind turbine should be adequately modeled for real-time simulation studies. This study proposes a simplified floating offshore wind turbine model, which is applicable for the real-time simulation of large-scale floating offshore wind farms. Two types of floating wind turbines are evaluated in this paper: the semi-submersible and spar-buoy floating wind turbines. The effectiveness of the simplified turbine models is shown by a comparison study with the detailed FAST (Fatigue, Aerodynamics, Structures, and Turbulence) floating turbine model. A large-scale floating offshore wind farm including eighty units of simplified turbines is tested in parallel simulation and real-time software (OPAL-RT). The wake effects among turbines and the effect of wind speeds on ocean waves are also taken into account in the modeling of offshore wind farms. Validation results show sufficient accuracy of the simplified models compared to detailed FAST models. The real-time results of offshore wind farms show the feasibility of the proposed turbine models for the real-time model of large-scale offshore wind farms.
The problem of damaged tendon diagnosis (damage detection, damaged tendon identification and damage precise quantification) in a new multibody offshore platform supporting a 10 MW Floating Offshore Wind Turbine (FOWT) is investigated for the first time in this study. Successful operation of the multibody FOWT depends on the integrity of its tendons connecting the upper and lower tanks of the platform. Thus, early diagnosis of the damaged tendons is of high importance and it is achieved through a vibration-based methodology. Damage detection is accomplished based on the detection of changes in the vibration response power spectral density, while damaged tendon identification and damage precise quantification are accomplished through the Functional Model Based Method (FMBM). The FMBM is appropriately formulated in this study to operate with only vibration response signals. The employed vibration responses under healthy and damaged states of the FOWT platform are obtained from a numerical model describing the platform's dynamics. Each examined damage scenario corresponds to the reduced stiffness at the connection point of a single tendon to the platform's upper tank. Subtle damages corresponding to a stiffness reduction of [10-25] %, have minor effects on the platform's dynamics due to the tendons' high strength, while damages corresponding to a stiffness reduction of [10-85] % on different tendons have similar effects on the dynamics, thus leading to an overall highly challenging diagnosis problem. The use of a single underwater accelerometer as well as a low and limited frequency bandwidth of surge acceleration signals, is explored. The results show that effective, reliable and very quick damaged tendon diagnosis is achieved via FMBM using the multibody FOWT platform's dynamics under damaged tendons.
Operation and maintenance constitute a substantial share of the lifecycle expenditures of an offshore renewable energy farm. A noteworthy number of methods and techniques have been developed to provide decision-making support in strategic planning and asset management. Condition monitoring instrumentation is commonly used, especially in offshore wind farms, due to the benefits it provides in terms of fault identification and performance evaluation and improvement. Incorporating technology advancements, a shift towards automation and digitalisation is taking place in the offshore maintenance sector. This paper reviews the existing literature and novel approaches in the operation and maintenance planning and the condition monitoring of offshore renewable energy farms, with an emphasis on the offshore wind sector, discussing their benefits and limitations. The state-of-the-art in industrial condition-based maintenance is reviewed, together with deterioration models and fault diagnosis and prognosis techniques. Future scenarios in robotics, artificial intelligence and data processing are investigated. The application challenges of these strategies and Industry 4.0 concepts in the offshore renewables sector are scrutinised, together with the potential implications of early-stage project integration. The identified technologies are ranked against a series of indicators, providing a reference for a range of industry stakeholders.
This study combined a Convolutional Neural Network (CNN) with the chaos theory and the Empirical Mode Decomposition (EMD) method for the attenuation fault recognition of power capacitors. First, it built six capacitor analysis models, including normal capacitors, failed capacitors, and normal capacitors attenuated by 20–80%. Then a power testing machine was used for an applied voltage test of the capacitor. The EMD method was combined with the chaos synchronisation detection method to chart the discharge signals of the voltage and current that was captured by a high frequency oscilloscope into a 3D chaotic error scatter plot, as the fault diagnosis feature image. Finally, the CNN algorithm was used for the capacitor fault detection. The advantages of the proposed method are that big data are compressed to extract meaningful feature images, the operating state of the power capacitor can be detected effectively, and faults can be diagnosed according to the electrical signal change of the power capacitor. The actual measurement results showed that the accuracy of the proposed method was as high as 97% and has a high efficiency of noise rejection ability, which indicates that the method could be applied to other power-related fields in the future. © 2021 The Authors. IET Science, Measurement & Technology published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology
Ice formed on wind turbines not only causes economic damage but also poses a risk to nearby humans. Countermeasuring ice formation requires reliable detection systems. Existing ice detection solutions have improved with time, but are still not sufficiently accurate. This paper investigates the use of different convolutional neural networks (CNNs) and RGB images taken from cameras installed on the nacelle of wind turbines to detect ice on the rotor blades. In our research, the VGG19 model achieved the best performance with an accuracy and an f1-score of (96 ± 2) %.
We prove that there does not exist non-constant positive $f$-harmonic function on the complete gradient shrinking Ricci solitons. We also prove the $L^{p}(p\geq 1 \ {\rm or}\ 0<p\leq 1)$ Liouville theorems on the complete gradient shrinking Ricci solitons.
Experience has shown that premature gearbox failures are a leading maintenance cost driver that can easily lower the profit margin from a wind turbine operation. Prognostics and Health Management (PHM) techniques offer the potential of effectively managing gearbox health problems by detecting early damage, tracking the severity of damage, estimating the time to reach pre-defined damage limits, and providing key information for proactive maintenance decisions. Experience has revealed that major damage modes of wind turbine gearboxes are bearing spall and gear teeth pitting, both of which release metallic debris particles in the oil lubrication system. Oil debris monitoring is thus well suited to provide an early indication and quantification of internal damage to bearings and gears of a wind turbine gearbox.This paper reviews the application of oil debris monitoring as an effective PHM solution for wind turbine gearboxes. The paper describes the principle of operation of the oil debris monitoring technology and the principle of application for effective PHM of wind turbine gearboxes. The paper explains the common surface fatigue damage mode of bearing and gear rolling elements and the characteristics of the destructive debris that result from this damage mode. The paper outlines a simple means of deriving accumulated debris count damage limits based upon basic gearbox component geometry and the use of moving averages for estimating rates of debris generation as a simple yet effective damage data-driven propagation model. Finally, the application of oil debris monitoring as an effective PHM technology for wind turbine gearboxes is illustrated by presenting actual data obtained from seeded fault bearing and gear tests and fielded gearbox applications.
An important path to reviewing technology is through failure investigation, which enables understanding of appropriate levels of safety and assurance of design criteria. In the occurrence of extreme environmental conditions in which most units respond well or at least satisfactorily, the assessment of exception cases can be taken as a good starting point for the failure investigation mentioned above. Hence, especially on account of hurricanes Rita and Katrina, the 2005 hurricane season, which had a notable impact on offshore industry at Gulf of Mexico region, can be taken as a promising scenario to assess the losses and bring out cases to be more carefully appraised. It is the case of the TLP Typhoon found upside down after the passage of Rita. Being the only unit located at a significant water depth which followed recent project standards, the case naturally came to the fore. This paper addresses an investigative analysis held out in order to establish and analyze the possible causes that resulted on the capsizing of the referred unit. The investigation focuses on two major paths which were selected as being the most probable and also had feasibility to be carried out — one that analyzes a possible lack of displacement due to the passage of a hurricane wave and the consequent loss of stability of the TLP and the other, concentrated on a dynamic analysis of the unit under the impact of the hurricane metocean conditions. This second analysis focuses on obtaining the tension on the tethers stating if either they suffered excessive loads causing them to break or lack of it, causing compression and possible buckling.
A new full time-domain nonlinear coupled method has been established and then applied to predict the responses of a Truss Spar in irregular wave. For the coupled analysis, a second-order time-domain approach is developed to calculate the wave forces, and a finite element model based on rod theory is established in three dimensions in a global coordinate system. In numerical implementation, the higher-order boundary element method (HOBEM) is employed to solve the velocity potential, and the 4th-order Adams-Bashforth-Moultn scheme is used to update the second-order wave surface. In deriving convergent solutions, the hull displacements and mooring tensions are kept consistent at the fairlead and the motion equations of platform and mooring-lines/risers are solved simultaneously using Newmark-β integration scheme including Newton-Raphson iteration. Both the coupled quasi-static analysis and the coupled dynamic analysis are performed. The numerical simulation results are also compared with the model test results, and they coincide very well as a whole. The slow-drift responses can be clearly observed in the time histories of displacements and mooring tensions. Some important characteristics of the coupled responses are concluded.
This report presents a comprehensive dynamic-response analysis of three offshore floating wind turbine concepts. Models were composed of one 5-MW turbine supported on land and three 5-MW turbines located offshore on a tension leg platform, a spar buoy, and a barge. A loads and stability analysis adhering to the procedures of international design standards was performed for each model using the fully coupled time-domain aero-hydro-servo-elastic design code FAST with AeroDyn and HydroDyn. The concepts are compared based on the calculated ultimate loads, fatigue loads, and instabilities. The results of this analysis will help resolve the fundamental design trade-offs between the floating-system concepts.
We present the first algorithms that allow the estimation of non-negative Lyapunov exponents from an experimental time series. Lyapunov exponents, which provide a qualitative and quantitative characterization of dynamical behavior, are related to the exponentially fast divergence or convergence of nearby orbits in phase space. A system with one or more positive Lyapunov exponents is defined to be chaotic. Our method is rooted conceptually in a previously developed technique that could only be applied to analytically defined model systems: we monitor the long-term growth rate of small volume elements in an attractor. The method is tested on model systems with known Lyapunov spectra, and applied to data for the Belousov-Zhabotinskii reaction and Couette-Taylor flow.
A numerical simulation method is presented by integrating Computational Fluid Dynamics (CFD) and Finite Element Analysis (FEA) solvers to predict ship wave loads and slamming loads taking into account hydroelastic effects. The interest of this study mainly lies in the slamming and green water pressures acting on a flexible ship investigated by the coupled CFD-FEA method. Firstly, verification and sensitivity analysis of the wave loads and slamming pressures on the S175 containership evaluated by the coupled CFD-FEA method is conducted by comparing the results using different mesh sizes and time step schemes. Discussion on the effect of hydroelasticity on impact pressures is also conducted. Then a comprehensive analysis on the global motions, wave loads, slamming and green water pressures of the ship in different regular wave conditions is undertaken. Finally, a simplified bow flare and bottom slamming pressure estimation method based on the seakeeping data of incident wave and ship global motions are proposed, which can reduce the computational burden of the two-way fluid-structure interaction simulations when impact pressure is concerned.
The reliable control of the large-scale wind farm is crucial for the stability and security of the renewable power system with high wind power penetration. Due to the uncertain and variable nature of wind power, the traditional control strategy is difficult to work. Regarding the large-scale, geographically dispersed wind farm, an efficient distributed economic model predictive control strategy is proposed, which integrates the power tracking and economic optimization of the wind farm into one optimal control framework. By adopting the global economic cost function, the Nash optimal solutions under distributed framework approach the Pareto optimum. Thus, the reference power from the transmission system operator is accurately tracked, while the global dynamic economic optimality is guaranteed. The simulation results under step wind speed and practical wind speed variations verify the efficiency and reliability of the proposed control strategy.
This paper provides an analytical proof and the theoretical development of the idea of using the torsional vibration measurements for a system-level condition monitoring of the drivetrain system. The method relies on modal parameter estimation of the drivetrain system by using the torsional measurements and subsequent monitoring of the variations in the system eigenfrequencies and normal modes. Angular velocity error function extracted from encoder outputs at both input and output of drivetrain is used to estimate modal parameters including natural frequencies and damping coefficients. In the proposed condition monitoring approach, it is shown that any abnormal deviation from the reference values of the drivetrain system dynamic properties can be translated into the progression of a specific fault in the system. In order to extract the condition monitoring features, local sensitivity analysis is engaged to establish a relationship between different categories of drivetrain faults with the system dynamic properties and the amplitude of torsional response, which helps with both to identify the state of the progressive faults and to localize them. Local sensitive analysis shows that abnormal deviations in stiffness and moment of inertia due to the presence of faults result in considerable changes in natural frequencies and modal responses which can be measured and used as fault detecting features by using the proposed analytical approach. Sensitivity analysis is also employed along with the estimated modal frequency for estimation of modal damping from the amplitude of response at the natural frequencies and their subsequent use for estimation of undamped natural frequencies which are later used in the proposed condition monitoring approach. The proposed approach is computationally inexpensive and can be implemented without additional instrumentation. Two test cases, using 10 MW simulated and 1.75 MW operational drivetrains have been demonstrated.
Machine learning techniques have been successfully applied for the intelligent fault diagnosis of rolling bearings in recent years. This study has developed an Improved Multi-Scale Convolutional Neural Network integrated with a Feature Attention mechanism (IMS-FACNN) model to address the poor performance of traditional CNN-based models under unsteady and complex working environments. The proposed IMS-FACNN has a good extrapolation performance because of the novel IMS coarse grained procedure with training interference and the introduced the feature attention mechanism, which improves the model’s generalization ability. The proposed IMS-FACNN model has a better performance than existing methods in all the examined scenarios including diagnosing the bearing fault of a real wind turbine. The results show that the reliability and superiority of the IMS-FACNN model in diagnosing faults of rolling bearings.
In the variable rotor speed and variable blade pitch wind-turbines system, the pitch controller is crucial in the high wind speed range to adjust the generator speed so as to produce the rated power. The main challenges in designing the pitch controller are the wind turbine’s nonlinearities, constraints on pitch-angle, the variations in wind-speed, and the unstructured model dynamics. From this perspective, a new pitch controller is proposed by employing partial offline quasi-min-max fuzzy model-predictive control to investigate the variable-speed wind turbine performance. Based on the fuzzy modeling, the online optimization problem is simplified as a partial offline optimization problem (offline design and online synthesis). The key advantage of this controller is the guaranteed stability with actuator constraints using LMI constraints and less computational burden. Also, this controller is compared with standard gain scheduled-PI (proportional integral) controller, which has been utilized profusely in the wind-turbine industry. Furthermore, a typical 5MW benchmark wind-turbine is employed to validate the results from the nonlinear mathematical model. Several case studies are made to prove the proposed controller effectiveness. The results show the superiority of the proposed controller over the gain scheduled-PI controller and two other advanced control techniques.
This paper studies the dynamic response of a spar-type direct-drive wind turbine subjected to external and internal excitations. A free-free end model is developed for the wind turbine structure with a spar-type floating platform under deep sea condition. Firstly, the spar supported platform with tower structure is modelled as a rigid body while the nacelle is considered as a point mass attached on the top of the tower. Then the dynamic interaction between the drive-train system and the tower is considered by incorporating the modelling of a direct-drive drive-train system. The hydrodynamic and aerodynamic excitations applied include current, wave, and wind excitations as well as buoyant forces. The misalignments of the wind, wave and current are also considered to examine the induced response. With the help of the time history and FFT spectrum, the effects of both hydrodynamic and aerodynamic excitations along with the dynamic interaction between the drive-train system and tower structure on the dynamic behaviour of the spar-type floating platform are investigated under different sea conditions.
Simulations are conducted in time domain to investigate the transient response of a SPAR-type floating offshore wind turbine in scenarios with fractured mooring lines. Towards this end, a coupled aero-hydro-elastic numerical model is developed. The methodology includes a blade-element-momentum model for aerodynamics, a nonlinear model for hydrodynamics, a nonlinear restoring model of SPAR buoy, and a fully nonlinear dynamic algorithm for intact and fractured mooring cables. The OC3 Hywind SPAR-type FOWT is chosen as an example to study the dynamic response after one of its mooring lines is suddenly broken. The motions of platform, the tensions in the mooring lines and the power generation performance are documented in different cases, including different fractured cables and different shutdown strategies. An interesting finding is that in terms of drift distance, it might be more dangerous to shut down the turbine in certain scenarios. Furthermore, the potential impacts of mooring failure on adjacent FOWTs are discussed for two designs of the wind farm.
In the present study, a series of numerical simulations of the performance changes of a Floating Offshore Wind Turbine (FOWT) with broken mooring line was carried out. For this simulation, an aero-hydro-servo-elastic-mooring coupled dynamic analysis were carried out in the time domain. The OC4 DeepCwind semisubmersible with NREL's 5-MW baseline turbine was selected as a reference platform. One of the three mooring lines was intentionally disconnected from the floating platform at a certain time. The resulting transient/unsteady responses and steady-state responses after that, mooring line tensions, and turbine performance were checked. The accidental disconnection of one of the mooring lines changes the watch circle of the floating platform and the tensions of the remaining mooring lines. In addition, the changes in the platform orientation also cause nacelle yaw error, which is directly related to the power production and structural fatigue life. When horizontal offset becomes large, power-line is likely to be disconnected and its influence was also investigated. To ensure the sustainability of a series of FOWTs associated with farm development, the influence of mooring line failure and resulting changes to the turbine performance should be checked in advance. Otherwise, successive failure of neighboring FOWTs could take place.
The paper investigates the acceleration of t-SNE - an embedding technique that is commonly used for the visualization of high-dimensional data in scatter plots-using two tree-based algorithms. In particular, the paper develops variants of the Barnes-Hut algorithm and of the dual-tree algorithm that approximate the gradient used for learning t-SNE embeddings in O(N log N ). Our experiments show that the resulting algorithms substantially accelerate t-SNE, and that they make it possible to learn embeddings of data sets with millions of objects. Somewhat counterintuitively, the Barnes-Hut variant of t-SNE appears to outperform the dual-tree variant.
The vast deepwater wind resource represents a potential to use offshore floating wind turbines to power much of the world with renewable energy. Many floating wind turbine concepts have been proposed, but dynamics models, which account for the wind inflow, aerodynamics, elasticity, and controls of the wind turbine, along with the incident waves, sea current, hydrodynamics, and platform and mooring dynamics of the floater, were needed to determine their technical and economic feasibility. This work presents the development of a comprehensive simulation tool for modeling the coupled dynamic response of offshore floating wind turbines, the verification of the simulation tool through model-to-model comparisons, and the application of the simulation tool to an integrated loads analysis for one of the promising system concepts. A fully coupled aero-hydro-servo-elastic simulation tool was developed with enough sophistication to address the limitations of previous frequency- and time-domain studies and to have the features required to perform loads analyses for a variety of wind turbine, support platform, and mooring system configurations. The simulation capability was tested using model-to-model comparisons. The favorable results of all of the verification exercises provided confidence to perform more thorough analyses. The simulation tool was then applied in a preliminary loads analysis of a wind turbine supported by a barge with catenary moorings. A barge platform was chosen because of its simplicity in design, fabrication, and installation. The loads analysis aimed to characterize the dynamic response and to identify potential loads and instabilities resulting from the dynamic couplings between the turbine and the floating barge in the presence of combined wind and wave excitation. The coupling between the wind turbine response and the barge-pitch motion, in particular, produced larger extreme loads in the floating turbine than experienced by an equivalent land-based turbine. Instabilities were also found in the system. The influence of conventional wind turbine blade-pitch control actions on the pitch damping of the floating turbine was also assessed. Design modifications for reducing the platform motions, improving the turbine response, and eliminating the instabilities are suggested. These suggestions are aimed at obtaining cost-effective designs that achieve favorable performance while maintaining structural integrity.
This work presents a comprehensive dynamic–response analysis of three offshore floating wind turbine concepts. Models were composed of one 5 MW turbine supported on land and three 5 MW turbines located offshore on a tension leg platform, a spar buoy and a barge. A loads and stability analysis adhering to the procedures of international design standards was performed for each model using the fully coupled time domain aero-hydro-servo-elastic simulation tool FAST with AeroDyn and HydroDyn. The concepts are compared based on the calculated ultimate loads, fatigue loads and instabilities. The loads in the barge-supported turbine are the highest found for the three floating concepts. The differences in the loads between the tension leg platform–supported turbine and spar buoy–supported turbine are not significant, except for the loads in the tower, which are greater in the spar system. Instabilities in all systems also must be resolved. The results of this analysis will help resolve the fundamental design trade-offs between the floating-system concepts. Copyright © 2011 John Wiley & Sons, Ltd.
The application of control techniques to offshore wind turbines has the potential to significantly improve the structural response, and thus reliability, of these systems. Passive and active control is investigated for a floating barge-type wind turbine. Optimal passive parameters are determined using a parametric investigation for a tuned mass damper system. A limited degree of freedom model is identified with synthetic data and used to design a family of controllers using H∞ multivariable loop shaping. The controllers in this family are then implemented in full degree of freedom time domain simulations. The performance of the passive and active control is quantified using the reduction in fatigue loads of the tower base bending moment. The performance is calculated as a function of active power consumption and the stroke of the actuator. The results are compared to the baseline and optimal passive system, and the additional achievable load reduction using active control is quantified. It is shown that the optimized passive system results in tower fore-aft fatigue load reductions of approximately 10% compared to a baseline turbine. For the active control, load reductions of 30% or more are achievable, at the expense of active power and large strokes. Active control is shown to be an effective means of reducing structural loads, and the costs in power and stroke to achieve these reductions are demonstrated.
Recent work has shown that several well-known models in the system dynamics literature contain previously unsuspected regimes of deterministic chaos. Two of the most extensively analyzed are Sterman's model of the economic long wave and the production-distribution model of the Beer Distribution Game. The significance of these theoretical developments hinges on whether the chaotic regimes lie in the realistic region of parameter space. There are also questions regarding the descriptive accuracy of models of human systems that exhibit chaos. Because of data limitations and the inability to conduct controlled experiments, empirical studies at the aggregate level are not likely to resolve these questions.
An alternative approach is based on laboratory experiments in which models provide a simulated environment for the study of human decisionmaking behavior. Recently, laboratory experiments have been conducted to analyze decision-making behavior in the longwave model and the Beer Distribution Game. This article describes these experiments and shows that the behavior of the subjects is explained well with a simple heuristic long used in system dynamics modeling and well grounded in behavioral decision theory. The parameters of the proposed decision rule are estimated econometrically for each subject. The parameters that characterize a significant minority of the subjects are shown to produce chaos. This direct experimental evidence that chaos can be produced by the decision-xnaking behavior of real people has important implications for the formulation, analysis, and testing of models of human behavior.
Chaotic time series data are observed routinely in experiments on physical systems and in observations in the field. The authors review developments in the extraction of information of physical importance from such measurements. They discuss methods for (1) separating the signal of physical interest from contamination ("noise reduction"), (2) constructing an appropriate state space or phase space for the data in which the full structure of the strange attractor associated with the chaotic observations is unfolded, (3) evaluating invariant properties of the dynamics such as dimensions, Lyapunov exponents, and topological characteristics, and (4) model making, local and global, for prediction and other goals. They briefly touch on the effects of linearly filtering data before analyzing it as a chaotic time series. Controlling chaotic physical systems and using them to synchronize and possibly communicate between source and receiver is considered. Finally, chaos in space-time systems, that is, the dynamics of fields, is briefly considered. While much is now known about the analysis of observed temporal chaos, spatio-temporal chaotic systems pose new challenges. The emphasis throughout the review is on the tools one now has for the realistic study of measured data in laboratory and field settings. It is the goal of this review to bring these tools into general use among physicists who study classical and semiclassical systems. Much of the progress in studying chaotic systems has rested on computational tools with some underlying rigorous mathematics. Heuristic and intuitive analysis tools guided by this mathematics and realizable on existing computers constitute the core of this review.
Influence of the WRF model and atmospheric reanalysis on the offshore wind resource potential and cost estimation: a case study for Rio de Janeiro State
- Luiz
Modelling and Analysis of 8psk modulation scheme using awgn channel for wcdma system
- Mc
Crack diagnosis method of wind turbine blade based on convolution neural network with 3D vibration information fusion
- Guo