1,721,070 research outputs found
Fault Detection, Supervision and Safety for Energy Conversion Systems: Wind Turbines and Hydroelectric Plants
No full textThe motivation for this article comes from a real need to have an open discussion about
the challenges of fault detection and supervision for very demanding systems, such as energy
conversion systems. These features represent the key characteristic to identify possible malfunctions
affecting the system (i.e. the so-called faults) and, at the same time, the capability to continue
working while maintaining power conversion efficiency, if proper countermeasures are adopted.
Moreover, the safety issue has begun to stimulate research and development in a wide range of
industrial communities particularly for those systems demanding a high degree of reliability and
availability, such as wind turbines and hydroelectric plants. In fact, once the faults are promptly
detected and compensated, the system will be able to maintain specified operable and committable
conditions, and at the same time should avoid expensive maintenance works. For very large
installations a clear conflict exists between ensuring a high degree of availability and reducing
costly maintenance, thus justifying the solutions addressed in the proposed contribution
Diagnosis and Fault‐tolerant Control 1: Data‐driven and Model‐based Fault Diagnosis Techniques
No full textThis book presents recent advances in fault diagnosis strategies for complex dynamic systems. Its impetus derives from the need for an overview of the challenges of the fault diagnosis technique, especially for those demanding systems that require reliability, availability, maintainability and safety to ensure efficient operations. Moreover, the need for a high degree of tolerance with respect to possible faults represents a further key point, primarily for complex systems, as modeling and control are inherently challenging, and maintenance is both expensive and safety-critical.
Diagnosis and Fault-tolerant Control 1 also presents and compares different diagnosis schemes using established case studies that are widely used in related literature. The main features of this book regard the analysis, design and implementation of proper solutions for the problems of fault diagnosis in safety critical systems. The design of the considered solutions involves robust data-driven, model-based approaches
Diagnosis and Fault‐tolerant Control 2: From Fault Diagnosis to Fault‐tolerant Control
No full textThis book presents recent advances in fault diagnosis and fault-tolerant control of dynamic processes. Its impetus derives from the need for an overview of the challenges of the fault diagnosis technique and sustainable control, especially for those demanding systems that require reliability, availability, maintainability, and safety to ensure efficient operations. Moreover, the need for a high degree of tolerance with respect to possible faults represents a further key point, primarily for complex systems, as modeling and control are inherently challenging, and maintenance is both expensive and safety-critical.
Diagnosis and Fault-tolerant Control 2 also presents and compares different fault diagnosis and fault-tolerant schemes, using well established, innovative strategies for modeling the behavior of the dynamic process under investigation. An updated treatise of diagnosis and fault-tolerant control is addressed with the use of essential and advanced methods including signal-based, model-based and data-driven techniques. Another key feature is the application of these methods for dealing with robustness and reliability
Advanced control design and fault diagnosis
Full text linkThis document provides the motivations and a brief introduction to the Special Issue entitled “Advanced Control Design and Fault Diagnosis”, which aims at presenting several solutions to the advanced control design and fault diagnosis systems. These methodologies can be considered in the general framework of advanced control, fault diagnosis and fault tolerant control systems, which are also able to improve the safety of the system under monitoring. The focuses of the current research in this field addressed in this Special Issue are also presented with emphasis on the practical application to simulated and realistic examples, which should provide an overall picture of current and future developments in this area. The works of this Special Issue represent suitably extended contributions selected by the proponents from the ACD2019—the 15th European Workshop on Advanced Control and Diagnosis, which was organised in Bologna, Italy on 21st–22nd November
Residual design for dynamic processes using de-coupling technique
No full textThe work presents some results concerning a fault detection scheme for dynamic processes using disturbance decoupling technique. The first step of the considered approach consists of exploiting input-output descriptions of the monitored system. In particular, the disturbance term of that model can be used to take into account unknown inputs affecting the system. The next step of the scheme leads to define a set of parity relations that can be used as residual signals since they are insensitive to the disturbance term. The proposed fault detection procedure has been tested on an industrial process simulator. Sensor and actuator faults have been simulated on a gas turbine model. Simulation results and concluding remarks have been finally reported
Foreword [to Fault Detection, Supervision and Safety for Technical Processes - 12th SAFEPROCESS 2024 Proceedings]
No full textIFAC SAFEPROCESS is a major international gathering of leading experts in academia and industry. It aims at strengthening contacts between academia and industry to build up new networks and cultivate existing relations. High-level speakers will present the global spectrum of fault diagnosis, process supervision and safety monitoring, state-of-the-art applications, and emerging research directions. The symposium is also meant as a forum for young scientists from all over the world, with the opportunity to introduce their research projects and works to an audience of international experts, young researchers, academics, and students. Fault diagnosis, Fault Detection and Isolation (FDI) and Fault-Tolerant Control (FTC) build a major area of research at the intersection of systems and control engineering, artificial intelligence, applied mathematics and statistics, and application fields like chemical, electrical, mechanical, aerospace engineering and transportation systems. IFAC has recognized the significance of this area by launching a
triennial symposium series dedicated to these subjects. SAFEPROCESS 2024 is continuing the successful series of symposia. The IFAC SAFEPROCESS 2024 edition will be focused on major topics, i.e., energy, cybersecurity, water systems, and autonomous vehicles. Special sessions, plenary lectures, tutorials, benchmarks, and roundtables will highlight industrial-academic projects, challenges, and applications
Artificial Intelligence Tools for Wind Turbine Blade Monitoring
No full textElectricity from wind turbines is popular and ecologically friendly. These gadgets must be reliable owing to the extensive usage of innovative materials. Researchers are creating efficient and cost-effective monitoring solutions for wind turbine blades, the most expensive part of a wind turbine. This study introduces a deep convolutional neural network-based wind turbine blade monitoring system based on medical auscultation. The system balances engineering dependability with economic efficiency. A lightweight architecture for monitoring wind turbine blades using edge computing and programmable logic controller signals is described in this study. Aerodynamic acoustic waves are collected and filtered by this technology. Our audio enhancement approaches combine self-adaptive mask targeting, multi-scale feature extraction, and deep neural networks to reduce wind turbine blade audio signal noise. Finally, we provide a unique technique to compress deep convolutional neural networks for peripheral computing devices with limited resources. Additionally, we optimise audio-generated spectrograms for wind turbine blade trouble diagnosis
Data–Driven Design of an Active Wake Steering Control for a Wind Farm Benchmark
No full textWake steering yaws upstream wind turbines to deflect their wakes from downstream turbines, thus increasing the generated power. However, most wake steering methods rely on lookup tables obtained offline, which map a set of conditions, such as wind speed and direction, to yaw angles for each turbine in a farm. These tables assume all turbines are operational and can be significantly non–optimal when one or more turbines do not provide the rated power, because of low wind speed, faults, routine maintenance, or emergency maintenance. This work presents an intelligent wake steering method that adapts to turbine actual working conditions when determining yaw angles. Using a hybrid model–and a learning–based method, i.e. an active control, a neural network is trained online to determine yaw angles from operating conditions including turbine status. Unlike purely model–based approaches which use lookup tables provided by the wind turbine manufacturer or generated offline, the proposed control solution does not need to solve e.g. optimisation problems for each combination of the turbine non-optimal working conditions in a farm; the integration of learning strategy in the control design allows to obtain an active control scheme
Model-Free Adaptive Fault-Tolerant Control for Offshore Wind Turbines
No full textFloating offshore wind turbines have increased in popularity owing to their adaptability for deep water applications and high power generation efficiency. The control of floating offshore wind turbines, on the other hand, is very complex. The main challenges are the difficulty in precisely modeling floating offshore wind turbines and the higher failure rate of components. As a consequence, this study proposes a model-free adaptive fault-tolerant control system for blade root moment sensor failures. A model-free adaptive control approach is used to construct an individual pitch controller and a fault compensation to avoid mathematical modeling of floating offshore wind turbines. The proposed fault-tolerant control technique removes the need for fault detection and isolation by converting the fault dynamic compensation process into a real-time control issue for nonlinear systems. The fatigue, aerodynamics, structures, and turbulence code simulates and tests the proposed control strategy, and the results show that the proposed strategy can not only keep the wheel bearing load balanced, but also reduce the movement of the floating platform and significantly reduce the bearing load of the floating offshore wind turbines. Furthermore, the output power is closer to the rated power, proving the strategy’s high fault tolerance in the face of repeated blade root moment sensor faults
Intelligent Fault Diagnosis Techniques Applied to an Offshore Wind Turbine System
Full text linkFault diagnosis of wind turbine systems is a challenging process, especially for offshore plants, and the search for solutions motivates the research discussed in this paper. In fact, these systems must have a high degree of reliability and availability to remain functional in specified operating conditions without needing expensive maintenance works. Especially for offshore plants, a clear conflict exists between ensuring a high degree of availability and reducing costly maintenance. Therefore, this paper presents viable fault detection and isolation techniques applied to a wind turbine system. The design of the so-called fault indicator relies on an estimate of the fault using data-driven methods and effective tools for managing partial knowledge of system dynamics, as well as noise and disturbance effects. In particular, the suggested data-driven strategies exploit fuzzy systems and neural networks that are used to determine nonlinear links between measurements and faults. The selected architectures are based on nonlinear autoregressive with exogenous input prototypes, which approximate dynamic relations with arbitrary accuracy. The designed fault diagnosis schemes were verified and validated using a high-fidelity simulator that describes the normal and faulty behavior of a realistic offshore wind turbine plant. Finally, by accounting for the uncertainty and disturbance in the wind turbine simulator, a hardware-in-the-loop test rig was used to assess the proposed methods for robustness and reliability. These aspects are fundamental when the developed fault diagnosis methods are applied to real offshore wind turbines
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