Fault Diagnosis and Fault Tolerant Control of Wind Turbines: An Overview

Wind turbines are playing an increasingly important role in renewable power generation. Their complex and large-scale structure, however, and operation in remote locations with harsh environmental conditions and highly variable stochastic loads make fault occurrence inevitable. Early detection and location of faults are vital for maintaining a high degree of availability and reducing maintenance costs. Hence, the deployment of algorithms capable of continuously monitoring and diagnosing potential faults and mitigating their effects before they evolve into failures is crucial. Fault diagnosis and fault tolerant control designs have been the subject of intensive research in the past decades. Significant progress has been made and several methods and control algorithms have been proposed in the literature. This paper provides an overview of the most recent fault diagnosis and fault tolerant control techniques for wind turbines. Following a brief discussion of the typical faults, the most commonly used model-based, data-driven and signal-based approaches are discussed. Passive and active fault tolerant control approaches are also highlighted and relevant publications are discussed. Future development tendencies in fault diagnosis and fault tolerant control of wind turbines are also briefly stated. The paper is written in a tutorial manner to provide a comprehensive overview of this research topic

Soft Computing Techniques and Their Applications in Intel-ligent Industrial Control Systems: A Survey

Soft computing involves a series of methods that are compatible with imprecise information and complex human cognition. In the face of industrial control problems, soft computing techniques show strong intelligence, robustness and cost-effectiveness. This study dedicates to providing a survey on soft computing techniques and their applications in industrial control systems. The methodologies of soft computing are mainly classified in terms of fuzzy logic, neural computing, and genetic algorithms. The challenges surrounding modern industrial control systems are summarized based on the difficulties in information acquisition, the difficulties in modeling control rules, the difficulties in control system optimization, and the requirements for robustness. Then, this study reviews soft-computing-related achievements that have been developed to tackle these challenges. Afterwards, we present a retrospect of practical industrial control applications in the fields including transportation, intelligent machines, process industry as well as energy engineering. Finally, future research directions are discussed from different perspectives. This study demonstrates that soft computing methods can endow industry control processes with many merits, thus having great application potential. It is hoped that this survey can serve as a reference and provide convenience for scholars and practitioners in the fields of industrial control and computer science

Fuzzy Controllers

Trying to meet the requirements in the field, present book treats different fuzzy control architectures both in terms of the theoretical design and in terms of comparative validation studies in various applications, numerically simulated or experimentally developed. Through the subject matter and through the inter and multidisciplinary content, this book is addressed mainly to the researchers, doctoral students and students interested in developing new applications of intelligent control, but also to the people who want to become familiar with the control concepts based on fuzzy techniques. Bibliographic resources used to perform the work includes books and articles of present interest in the field, published in prestigious journals and publishing houses, and websites dedicated to various applications of fuzzy control. Its structure and the presented studies include the book in the category of those who make a direct connection between theoretical developments and practical applications, thereby constituting a real support for the specialists in artificial intelligence, modelling and control fields

Design and development of intelligent actuator control methodologies for morphing wing in wind tunnel

In order to protect our environment by reducing the aviation carbon emissions and making the airline operations more fuel efficient, internationally, various collaborations were established between the academia and aeronautical industries around the world. Following the successful research and development efforts of the CRIAQ 7.1 project, the CRIAQ MDO 505 project was launched with a goal of maximizing the potential of electric aircraft. In the MDO 505, novel morphing wing actuators based on brushless DC motors are used. These actuators are placed chord-wise on two actuation lines. The demonstrator wing, included ribs, spars and a flexible skin, that is composed of glass fiber. The 2D and 3D models of the wing were developed in XFOIL and Fluent. These wing models can be programmed to morph the wing at various flight conditions composed of various Mach numbers, angles of attack and Reynolds number by allowing the computation of various optimized airfoils. The wing was tested in the wind tunnel at the IAR NRC Ottawa. In this thesis actuators are mounted with LVDT sensors to measure the linear displacement. The flexible skin is embedded with the pressure sensors to sense the location of the laminar-to-turbulent transition point. This thesis presents both linear and nonlinear modelling of the novel morphing actuator. Both classical and modern Artificial Intelligence (AI) techniques for the design of the actuator control system are presented. Actuator control design and validation in the wind tunnel is presented through three journal articles; The first article presents the controller design and wind tunnel testing of the novel morphing actuator for the wing tip of a real aircraft wing. The new morphing actuators are made up of BLDC motors coupled with a gear system, which converts the rotational motion into linear motion. Mathematical modelling is carried out in order to obtain a transfer function based on differential equations. In order to control the morphing wing it was concluded that a combined position, speed and current control of the actuator needs to be designed. This controller is designed using the Internal Model Control (IMC) method for the linear model of the actuator. Finally, the bench testing of the actuator is carried out and is further followed by its wind testing. The infra red thermography and kulite sensors data revealed that on average on all flight cases, the laminar to turbulent transition point was delayed close to the trailing edge of the wing. The second journal article presents the application of Particle Swarm Optimization (PSO) to the control design of the novel morphing actuator. Recently PSO algorithm has gained reputation in the family of evolutionary algorithms in solving non-convex problems. Although it does not guarantee convergence, however, by running it several times and by varying the initialization conditions the desired results were obtained. Following the successful computation of controller design, the PSO was validated using successful bench testing. Finally, the wind tunnel testing was performed based on the designed controller, and the Infra red testing and kulite sensor measurements results revealed the expected extension of laminar flows over the morphing wing. The third and final article presents the design of fuzzy logic controller. The BLDC motor is coupled with the gear which converts the rotary motion into linear motion, this phenomenon is used to push and pull the flexible morphing skin. The BLDC motor itself and its interaction with the gear and morphing skin, which is exposed to the aerodynamic loads, makes it a complex nonlinear system. It was therefore decided to design a fuzzy controller, which can control the actuator in an appropriate way. Three fuzzy controllers were designed each of these controllers was designed for current, speed and position control of the morphing actuator. Simulation results revealed that the designed controller can successfully control the actuator. Finally, the designed controller was tested in the wind tunnel; the results obtained through the wind tunnel test were compared, and further validated with the infra red and kulite sensors measurements which revealed improvement in the delay of transition point location over the morphed wing

Robust fractional-order fast terminal sliding mode control with fixed-time reaching law for high-performance nanopositioning

Open Access via the Wiley Agreement ACKNOWLEDGEMENTS This work is supported by the China Scholarship Council under Grant No. 201908410107 and by the National Natural Science Foundation of China under Grant No. 51505133. The authors also thank the anonymous reviewers for their insightful and constructive comments.Peer reviewedPublisher PD

System identification of a hysteresis-controlled pump system using SINDy

Hysteresis-controlled devices are widely used in industrial applications. For example, cooling devices usually contain a two-point controller, resulting in a nonlinear hybrid system with two discrete states. Dynamic models of systems are essential for optimizing such industrial supply technology. However, conventional system identification approaches can hardly handle hysteresis-controlled devices. Thus, the new identification method Sparse Identification of Nonlinear Dynamics (SINDy) is extended to consider hybrid systems. SINDy composes models from basis functions out of a customized library in a data-driven manner. For modeling systems that behave dependent on their own past as in the case of natural hysteresis, Ferenc Preisach introduced the relay hysteron as an elementary mathematical description. In this new method (SINDyHybrid), tailored basis functions in form of relay hysterons are added to the library which is used by SINDy. Experiments with a hysteresis controlled water basin show that this approach correctly identifies state transitions of hybrid systems and also succeeds in modeling the dynamics of the discrete system states. A novel proximity hysteron achieves the robustness of this method. The impacts of the sampling rate and the signal noise ratio of the measurement data are examined accordingly

Advanced Mathematics and Computational Applications in Control Systems Engineering

Control system engineering is a multidisciplinary discipline that applies automatic control theory to design systems with desired behaviors in control environments. Automatic control theory has played a vital role in the advancement of engineering and science. It has become an essential and integral part of modern industrial and manufacturing processes. Today, the requirements for control precision have increased, and real systems have become more complex. In control engineering and all other engineering disciplines, the impact of advanced mathematical and computational methods is rapidly increasing. Advanced mathematical methods are needed because real-world control systems need to comply with several conditions related to product quality and safety constraints that have to be taken into account in the problem formulation. Conversely, the increment in mathematical complexity has an impact on the computational aspects related to numerical simulation and practical implementation of the algorithms, where a balance must also be maintained between implementation costs and the performance of the control system. This book is a comprehensive set of articles reflecting recent advances in developing and applying advanced mathematics and computational applications in control system engineering

System identification of a hysteresis-controlled pump system using SINDy

Hysteresis-controlled devices are widely used in industrial applications. For example, cooling devices usually contain a two-point controller, resulting in a nonlinear hybrid system with two discrete states. Dynamic models of systems are essential for optimizing such industrial supply technology. However, conventional system identification approaches can hardly handle hysteresis-controlled devices. Thus, the new identification method Sparse Identification of Nonlinear Dynamics (SINDy) is extended to consider hybrid systems. SINDy composes models from basis functions out of a customized library in a data-driven manner. For modeling systems that behave dependent on their own past as in the case of natural hysteresis, Ferenc Preisach introduced the relay hysteron as an elementary mathematical description. In this new method (SINDyHybrid), tailored basis functions in form of relay hysterons are added to the library which is used by SINDy. Experiments with a hysteresis controlled water basin show that this approach correctly identifies state transitions of hybrid systems and also succeeds in modeling the dynamics of the discrete system states. A novel proximity hysteron achieves the robustness of this method. The impacts of the sampling rate and the signal noise ratio of the measurement data are examined accordingly

Development and validation of control methods for an actuation system in a morphing wing and aileron system

Morphing wing technology is one of the most efficient approaches to reduce fuel consumption and air pollution. The project, called “CRIAQ MDO 505”, was created to explore and evaluate the morphing wing technology. A wing tip system composed of a wing and an aileron was designed and manufactured by the CRIAQ team at the LARCASE. In the context of this project, an optimization approach was studied to improve the aerodynamic performance by changing a wing’s shape. Different methodologies were applied to control four internal actuators attached inside the morphing wing. These actuators morph the upper skin of the wing so that the transition region moves from the wing leading edge to its trailing edge. The research presented here is a part of the MDO 505 project. The aim of this research is to model, simulate and validate the control methods for the wing-tip morphing control system. ANFIS (Adaptive Neuro-Fuzzy Inference System), an adaptive control algorithm, was selected for the morphing wing-tip control. A combination of neural networks and adaptive fuzzy control, ANFIS takes advantage of the fuzzy inference system (FIS) and of the selflearning abilities of the neural network, and thus offers a promising approach for the stability and accuracy of the proposed control system. The simulation and experimental results were acquired using National Instruments (NI) Veristand, Maxon drives and MATLAB/Simulink software. Experimental tests were carried out at the IAR-NRC Wind Tunnel in Ottawa to validate the simulation results. The results showed the potential for applying intelligent control methods to improve the performance of aircraft using morphing wing technology