1,720,996 research outputs found
Robust Fault Diagnosis and Fault Tolerant Control of Wind Turbines
No full textThe increasing demand for energy generation from renewable sources has led to a growing attention on wind turbines. They represent very complex systems which require reliability, availability, maintainability, safety and, above all, efficiency on the generation of electrical power. Thus, new research challenges arise in the context of modelling and control. Advanced sustainable control systems can provide the optimisation of energy conversion and guarantee the desired performances even in presence of possible anomalous working condition, caused by unexpected faults. This monograph deals with the fault diagnosis and the fault tolerant control of wind turbines, and it proposes novel solutions to the problem of earlier fault detection and accommodation. The developed fault tolerant controller is mainly based on a fault diagnosis module, which provides the on-line information on the faulty or fault-free status of the system, so that the control action can be compensated. The design of the fault estimators involves different approaches, as they offer an effective tool for coping with a poor analytical knowledge of the system dynamics, together with noise and disturbances
Data-Driven Fault Diagnosis and Fault Tolerant Control of Wind Turbines
No full textNell’ultimo decennio, la crescente richiesta di produzione di energia elettrica da fonti rinnovabili, ha generato una cospicua attenzione nei riguardi delle turbine eoliche. Si tratta di sistemi particolarmente complessi, che richiedono affidabilit`a, sicurezza, manutenzione e, soprattutto, efficienza nella produzione di potenza elettrica. Pertanto, sono sorte nuove sfide nel campo della ricerca e sviluppo, in particolare nel contesto della modellazione e del controllo. Sistemi di controllo sostenibile e all’avanguardia possono ottimizzare la conversione di energia e garantire determinate prestazioni, anche in presenza di condizioni di lavoro anomale, causate da malfunzionamenti e guasti inaspettati. Questa tesi tratta la tematica della diagnosi dei guasti e del controllo tollerante al guasto applicato alle turbine eoliche. Si propongono originali soluzioni relative al problema della pronta rivelazione del guasto e del suo trattamento. Il sistema di controllo che si `e sviluppato `e principalmente basato su un modulo di diagnosi del guasto, che ha il compito di fornire in tempo reale l’informazione sull’eventuale guasto presente, in modo da compensare l’azione di controllo. Il progetto degli stimatori di guasto riguarda strategie basate sui dati, poich ́e offrono un efficace strumento per la gestione di sistemi le cui dinamiche sono scarsamente conosciute in termini analitici e presentano rumore e disturbi. Il primo di questi approcci basati sui dati `e ottenuto tramite modelli fuzzy Takagi-Sugeno (TS), derivanti dall’algoritmo di clustering c-means, seguito da una procedura di identificazione dei parametetri che risolve il problema della reiezione dei disturbi. Il secondo metodo proposto si serve di reti neurali artificiali per descrivere le relazioni fortemente non lineari che sussistono fra misure e guasti. L’architettura scelta fa parte della topologia Non lineare Autoregressiva con ingresso esogeno (NARX), dato che pu`o rappresentare l’evoluzione dinamica di un sistema nel tempo. L’addestramento della rete neurale sfrutta l’algoritmo di Levenberg-Marquardt con backpropagation, e processa un insieme di dati-obiettivo direttamente acquisiti. Gli schemi di diagnosi del guasto e controllo tollerante al guasto sono stati testati per mezzo di due modelli benchmark ad alta fedelt`a, i quali simulano rispettivamente il comportamento di una singola turbina e di un parco eolico, sia in condizioni normali, sia di guasto. Le prestazioni ottenute sono state confrontate con quelle di altre strategie di controllo, proposte in letteratura. Inoltre, un’analisi Monte Carlo ha validato la robustezza dei sistemi sviluppati, relativa a tipiche variazioni nei parametri, disturbi e incertezze.
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Infine, si `e effettuato un test Hardware In the Loop (HIL), al fine di valutare le prestazioni in un contesto piu` realistico e real-time. L’efficacia mostrata dai risultati ottenuti suggerisce future ricerche sull’effettiva applicabilit`a industriale dei sistemi proposti.In recent years, the increasing demand for energy generation from renewable sources has led to a growing attention on wind turbines. Indeed, they represent very complex systems which require reliability, availability, maintainability, safety and, above all, efficiency on the generation of electrical power. Thus, new research challenges arise, in particular in the context of modeling and control. Advanced sustainable control systems can provide the optimization of energy conversion and guarantee the desired performances even in presence of possible anomalous working condition, caused by unexpected faults and malfunctions. This thesis deals with the fault diagnosis and the fault tolerant control of wind turbines, and it proposes novel solutions to the problem of earlier fault detection and accommodation. The developed fault tolerant controller is mainly based on a fault diagnosis module, that provides the on-line information on the faulty or fault-free status of the system, so that the controller action can be compensated. The design of the fault estimators involves data-driven approaches, as they offer an effective tool for coping with a poor analytical knowledge of the system dynamics, together with noise and disturbances. The first data-driven proposed solution relies on fuzzy Takagi-Sugeno (TS) models, that are derived from a clustering c-means algorithm, followed by an identification procedure solving the noise-rejection problem. Then, a second solution makes use of neural networks to describe the strongly nonlinear relationships between measurement and faults. The chosen network architecture belongs to the Nonlinear AutoRegressive with eXogenous input (NARX) topology, as it can represent a dynamic evolution of the system along time. The training of the neural network fault estimators exploits the backpropagation Levenberg-Marquardt algorithm, that processes a set of acquired target data. The developed fault diagnosis and fault tolerant control schemes are tested by means of two high-fidelity benchmark models, that simulate the normal and the faulty behavior of a single wind turbine and a wind farm, respectively. The achieved performances are compared with those of other control strategies, coming from the related literature. Moreover, a Monte Carlo analysis validates the robustness of the proposed systems against the typical parameter uncertainties and disturbances. Finally, the Hardware In the Loop (HIL) test is carried out, in order to assess the performance in a more realistic real-time framework. The effectiveness shown by the achieved results suggests further investigations on the industrial application of the proposed systems
Fault diagnosis and sustainable control of wind turbines: robust data-driven and model-based strategies
No full textFault Diagnosis and Sustainable Control of Wind Turbines: Robust Data-Driven and Model-Based Strategies discusses the development of reliable and robust fault diagnosis and fault-tolerant (‘sustainable’) control schemes by means of data-driven and model-based approaches. These strategies are able to cope with unknown nonlinear systems and noisy measurements. The book also discusses simpler solutions relying on data-driven and model-based methodologies, which are key when on-line implementations are considered for the proposed schemes. The book targets both professional engineers working in industry and researchers in academic and scientific institutions. In order to improve the safety, reliability and efficiency of wind turbine systems, thus avoiding expensive unplanned maintenance, the accommodation of faults in their early occurrence is fundamental. To highlight the potential of the proposed methods in real applications, hardware-in-the-loop test facilities (representing realistic wind turbine systems) are considered to analyze the digital implementation of the designed solutions. The achieved results show that the developed schemes are able to maintain the desired performances, thus validating their reliability and viability in real-time implementations. Different groups of readers-ranging from industrial engineers wishing to gain insight into the applications’ potential of new fault diagnosis and sustainable control methods, to the academic control community looking for new problems to tackle-will find much to learn from this work
Data-Driven and Model-Based Control Techniques for a Wind Turbine Benchmark Model
Full text linkAbstract: Wind turbine plants are complex dynamic and uncertain processes driven by stochastic inputs and disturbances, as well as different loads represented by gyroscopic, centrifugal, and gravitational forces. Moreover, as their aerodynamic models are nonlinear, both modelling and control become challenging problems. On one hand, high–fidelity simulators should contain different parameters and variables in order to accurately describe the main dynamic system behaviour. Therefore, the development of modelling and control for wind turbine systems should consider these complexity aspects. On the other hand, these control solutions have to include the main wind turbine dynamic characteristics without becoming too complicated. The main point of this paper is thus to provide two practical examples of development of robust control strategies when applied to a simulated wind turbine plant. Extended simulations with the wind turbine benchhmark model and the Monte–Carlo tool represent the instruments for assessing the robustness and reliability aspects of the developed control methodologies when the model–reality mismatch and measurement errors are also considered. Advantages and drawbacks of these regulation methods are also highlighted with respect to different control strategies via proper performance metrics
A low-cost high-fidelity ultrasound simulator with the inertial tracking of the probe pose
No full textThe authors developed a versatile ultrasound simulator. The proposed system achieves the main features of a high-fidelity device exploiting low-cost rapid prototyping hardware. The hand-guided ultrasound simulator probe includes a RFID reader, a 9-DOF inertial sensor unit, consisting of an accelerometer, a magnetometer and a gyroscope, and a microcontroller that performs the real-time data acquisition, the processing and the transmission of the estimated pose information to the visualization system, so that the proper ultrasound view can be generated. Since the probe orientation is the main information involved in the pose reconstruction, this work presents and investigates several tracking methods for the probe orientation, exploiting a sensor fusion technique to filter the noisy measurements coming from inertial sensors. The performances of a Kalman filter, a nonlinear complementary filter and a quaternion-based filter as inertial trackers have been tested by means of a robot manipulator, in terms of readiness, accuracy and stability of the estimated orientation signal. The results show that the nonlinear complementary filter and the quaternion-based filter match all the application requirements (RMSE <3deg, variance <1deg2, and settling time <0.3s), and they involve a lower computational time with respect to the Kalman filter
Data–Driven Wake Steering Control for a Simulated Wind Farm Model
No full textUpstream wind turbines yaw to divert their wakes away from downstream turbines, increasing the power produced. Nevertheless, the majority of wake steering techniques rely on offline lookup tables that translate a set of parameters, including wind speed and direction, to yaw angles for each turbine in a farm. These charts assume that every turbine is working well, however they may not be very accurate if one or more turbines are not producing their rated power due to low wind speed, malfunctions, scheduled maintenance, or emergency maintenance. This study provides an intelligent wake steering technique that, when calculating yaw angles, responds to the actual operating conditions of the turbine. A neural network is trained live to determine yaw angles from operating conditions, including turbine status, using a hybrid model and a learning-based method, i.e. an active control. The proposed control solution does not need to solve optimization problems for each combination of the turbines’ non-optimal working conditions in a farm; instead, the integration of learning strategy in the control design enables the creation of an active control scheme, in contrast to purely model-based approaches that use lookup tables provided by the wind turbine manufacturer or generated offline. The suggested methodology does not necessitate a substantial amount of training samples, unlike purely learning-based approaches like model-free reinforcement learning. In actuality, by taking use of the model during back propagation, the suggested approach learns more from each sample. Based on the flow redirection and induction in the steady state code, results are reported for both normal (nominal) wake steering with all turbines operating as well as defective conditions. It is a free tool for optimizing wind farms that The National Renewable Energy Laboratory (USA) offers. These yaw angles are contrasted and checked with those discovered through the resolution of an optimization issue. Active wake steering is made possible by the suggested solution, which employs a hybrid model and learning-based methodology, through sample efficient training and quick online evaluation. Finally, a hardware-in-the-loop test-bed is taken into consideration for assessing and confirming the performance of the suggested solutions in a more practical setting
Artificial Intelligence Tools for Actuator Fault Diagnosis of an Unmanned Underwater Vehicle
No full textThe paper addresses the development of an artificial intelligence algorithm implemented for maximum power point tracking control of a unmanned underwater vehicle. It is shown that this algorithm tracks the optimum operation point and provides fast response even in the presence of faults. The strategy implements the tracking algorithm by using real—time measurements, while providing maximum power to the grid without using online data training. The solution is simulated in the Matlab and Simulink to verify the effectiveness of the proposed approach when fault–free and faulty conditions are considered. The simulation results highlight efficient, intrinsic and passive fault tolerant performances of the algorithm for general unmanned underwater vehicles with low inertia. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland A
FUZZY MODELLING AND IDENTIFICATION FOR SUSTAINABLE CONTROL DESIGN OF AN OFFSHORE WIND FARM
No full textIn order to improve the safety, the reliability, and the efficiency of offshore wind park installations, thus
avoiding expensive unplanned maintenance, the accommodation of faults in their earlier occurrence is
fundamental. Therefore, the main contribution of this paper consists of the development of a fault
tolerant (the so-called ‘sustainable’) control scheme by means of a data-driven approach. In particular,
this strategy based on fuzzy model identification is exploited for deriving the mathematical description of
the required controller. Fuzzy theory is exploited here since it is able to approximate easily unknown
nonlinear systems and manage noisy measurements. Moreover, the fuzzy controller, which is directly
identified from the wind farm measurements, provides the straightforward achievement of the fault
tolerance feature. In general, an analytic approach, where the system nonlinearity is explicitly taken into
account, could require more complex design methodologies. This aspect of the work, followed by the
simpler solution relying on fuzzy rules, represents the key point when on-line implementations are
considered of the proposed control scheme. To highlight the potential of the proposed fault tolerant
control scheme in real applications, a Hardware–In–the–Loop test facility representing a realistic
offshore wind farm installation is considered to analyse the digital implementation of the designed
controller. The achieved results show that the developed scheme maintains desired performances, thus
validating its reliability and viability also in real-time implementations
Wind turbine simulator fault diagnosis via fuzzy modelling and identification techniques
No full textFor improving the safety and the reliability of wind turbine installations, the earliest and fastest fault
detection and isolation are highly required, since it could be used also for accommodation purpose.
Modern wind turbines consist of several important subsystems, which can be affected by malfunctions
regarding actuators, sensors, and components. From the turbine control point-of-view they are extremely
important since provide the actuation signals, the main functions, as well as the measurements. In this
paper, a fault diagnosis scheme based on the identification of fuzzy models is described, in order to
detect and isolate these faults in the most efficient way, in order also to improve the energy cost, the
production rate, and reduce the operation and maintenance operations. Fuzzy systems are proposed here
since the model under investigation is nonlinear, whilst the wind speed measurement is uncertain since it
depends on the rotor plane wind turbulence effects. These fuzzy models are described as Takagi–Sugeno
prototypes, whose parameters are estimated from the wind turbine measurements. The fault diagnosis
methodology is thus developed using these fuzzy models, which are exploited as residual generators. The
wind turbine simulator is finally employed for the validation of the obtained performances
Design of an ultrasound simulator with probe pose tracking and medical dataset processing and visualization
No full textA high-fidelity, low-cost ultrasound simulator has been developed in order to improve the efficiency of ultrasound training, particularly in Focus Assessment by Sonography in Trauma (FAST). This examination is rapidly increasing its diffusion in Emergency Medicine, as it represents a quick tool for the detection of free fluids. The probe is the main component of the simulator as it provides the on-line tracking of its pose relative to the manikin representing the virtual patient. The proposed algorithm for the estimation of the probe orientation, a nonlinear complementary filter, is based on the fusion of information coming from inertial sensors. It is tested in static and dynamic conditions and its performances are compared with another traditional algorithm: the Kalman filter. Moreover, the software for the processing of medical dataset as images, videos and 3D volumes, has been developed in order to display the appropriate visualization on the simulator monitor. Finally, the simulator has been tested in a training session
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