Fault diagnosis and sustainable control of wind turbines: Robust data-driven and model-based strategies

Fault 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 Wake Steering Control for a Simulated Wind Farm Model

Upstream 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

Data-Driven and Model-Based Control Techniques for a Wind Turbine Benchmark Model

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

Toward Future Automatic Warehouses: An Autonomous Depalletizing System Based on Mobile Manipulation and 3D Perception

This paper presents a mobile manipulation platform designed for autonomous depalletizing tasks. The proposed solution integrates machine vision, control and mechanical components to increase flexibility and ease of deployment in industrial environments such as warehouses. A collaborative robot mounted on a mobile base is proposed, equipped with a simple manipulation tool and a 3D in-hand vision system that detects parcel boxes on a pallet, and that pulls them one by one on the mobile base for transportation. The robot setup allows to avoid the cumbersome implementation of pick-and-place operations, since it does not require lifting the boxes. The 3D vision system is used to provide an initial estimation of the pose of the boxes on the top layer of the pallet, and to accurately detect the separation between the boxes for manipulation. Force measurement provided by the robot together with admittance control are exploited to verify the correct execution of the manipulation task. The proposed system was implemented and tested in a simplified laboratory scenario and the results of experimental trials are reported

Technical and Functional Validation of a Teleoperated Multirobots Platform for Minimally Invasive Surgery

Nowadays Robotic assisted Minimally Invasive Surgeries (R-MIS) are the elective procedures for treating highly accurate and scarcely invasive pathologies, thanks to their abil- ity to empower surgeons\u2019 dexterity and skills. The research on new Multi-Robots Surgery (MRS) platform is cardinal to the development of a new SARAS surgical robotic platform, which aims at carrying out autonomously the assistants tasks during R- MIS procedures. In this work, we will present the SARAS MRS platform validation protocol, framed in order to assess: (i) its technical performances in purely dexterity exercises, and (ii) its functional performances. The results obtained show a prototype able to put the users in the condition of accomplishing the tasks requested (both dexterity- and surgical-related), even with rea- sonably lower performances respect to the industrial standard. The main aspects on which further improvements are needed result to be the stability of the end effectors, the depth per- ception and the vision systems, to be enriched with dedicated virtual fixtures. The SARAS\u2019 aim is to reduce the main surgeon\u2019s workload through the automation of assistive tasks which would benefit both surgeons and patients by facilitating the surgery and reducing the operation time

Data-Driven Fault Diagnosis and Fault Tolerant Control of Wind Turbines

Nell’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. 1 2 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

Fuzzy and Neural Network Approaches to Wind Turbine Fault Diagnosis

The fault diagnosis of safety critical systems such as wind turbine installations includes extremely challenging aspects that motivate the research issues considered in this paper. Therefore, this work investigates two fault diagnosis solutions that exploit the direct estimation of the faults by means of data-driven approaches. In this way, the diagnostic residuals are represented by the reconstructed faults affecting the monitored process. The proposed methodologies are based on fuzzy systems and neural networks used to estimate the nonlinear dynamic relations between the input and output measurements of the considered process and the faults. To this end, the considered prototypes are integrated with auto-regressive with exogenous input descriptions, thus making them able to approximate unknown nonlinear dynamic functions with arbitrary degree of accuracy. These residual generators are estimated from the input and output measurements acquired from a high-fidelity benchmark that simulates the healthy and the faulty behaviour of a wind turbine system. The robustness and the reliability features of the developed solutions are validated in the presence of model-reality mismatch and modelling error effects featured by the wind turbine simulator. Moreover, a hardware-in-the-loop tool is implemented for testing and comparing the performance of the developed fault diagnosis strategies in a more realistic environment and with respect to different fault diagnosis approaches. The achieved results have demonstrated the effectiveness of the developed schemes also with respect to more complex model-based and data-driven fault diagnosis methodologies

Robust Fault Diagnosis and Fault Tolerant Control of Wind Turbines

The 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

Augmented Reality and Robotic Systems for Assistance in Percutaneous Nephrolithotomy Procedures: Recent Advances and Future Perspectives

Percutaneous nephrolithotomy is the gold standard for the treatment of renal stones larger than 20 mm in diameter. The treatment outcomes of PCNL are highly dependent on the accuracy of the puncture step, in order to achieve a suitable renal access and reach the stone with a precise and direct path. Thus, performing the puncturing to get the renal access is the most crucial and challenging step of the procedure with the steepest learning curve. Many simulation methods and systems have been developed to help trainees achieve the requested competency level to achieve a suitable renal access. Simulators include human cadavers, animal tissues and virtual reality simulators to simulate human patients. On the other hand, the availability of pre-operative information (e.g., computed tomography or magnetic resonance imaging) and of intra-operative images (e.g., ultrasound images) has allowed the development of solutions involving augmented reality and robotic systems to assist the surgeon during the operation and to help a novel surgeon in strongly reducing the learning curve. In this context, the real-time awareness of the 3D position and orientation of the considered anatomical structures with reference to a common frame is fundamental. Such information must be accurately estimated by means of specific tracking systems that allow the reconstruction of the motion of the probe and of the tool. This review paper presents a survey on the leading literature on augmented reality and robotic assistance for PCNL, with a focus on existing methods for tracking the motion of the ultrasound probe and of the surgical needle

Fault–Tolerant Control of Offshore Wind Farm Installations via Adaptive Nonlinear Filters

In order to improve the availability of offshore wind farms, thus avoiding unplanned operation and maintenance costs, which can be high for offshore installations, the accommodation of faults in their earlier occurrence is fundamental. Therefore, this paper addresses the design of an active fault tolerant control scheme that is applied to a small wind park benchmark of nine wind turbines, based on their nonlinear models, as well as the wind and interactions between the wind turbines in the wind farm. The controller accommodation scheme provides the on–line estimate of the fault signals generated by nonlinear filters exploiting the nonlinear geometric approach to obtain estimates decoupled from both model uncertainty and the interactions among the turbines. This paper proposes also a datadriven approach to provide these disturbance terms in analytical forms, which are subsequently used for designing the nonlinear filters for fault estimation. In general, purely analytic approaches, where the system nonlinearity and disturbance decoupling properties are explicitly considered, could require complex design strategies. This feature of the work, followed by the simpler solution relying on a data–driven approach, can represent the key point when on–line implementations are considered for a viable application of the proposed scheme. The wind farm benchmark is considered to validate the performances of the suggested scheme in the presence of different fault conditions, modelling and measurement errors