+353-1-416-8900REST OF WORLD
+44-20-3973-8888REST OF WORLD
1-917-300-0470EAST COAST U.S
1-800-526-8630U.S. (TOLL FREE)

Fault Diagnosis and Sustainable Control of Wind Turbines. Robust Data-Driven and Model-Based Strategies

  • Book

  • January 2018
  • Elsevier Science and Technology
  • ID: 4335160

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.

Please Note: This is an On Demand product, delivery may take up to 11 working days after payment has been received.

Table of Contents

1. Introduction
2. System and Fault Modelling
3. Data-Driven Modelling and Identification
4. Fault Diagnosis and Fault Tolerant Control Schemes
5. Nonlinear Geometric Approach for Fault Diagnosis
6. Simulations, Experiments and Results
7. Conclusions

Authors

Silvio Simani Assistant Professor, Department of Engineering, University of Ferrara. Dr. Silvio Simani received his Laurea degree (cum laude) in Electronic Engineering from the Department of Engineering at the University of Ferrara, Italy, in 1996, and was awarded the Ph.D. in Information Science (Automatic Control) at the Department of Engineering of the University of Ferrara and Modena, Italy, in 2000. Since February 2002 he has been Assistant Professor at the Department of Engineering of the University of Ferrara. He has published about 240 refereed journal and conference papers, several book's chapters, and 3 monographs. His research interests include fault diagnosis and fault tolerant control of linear and nonlinear dynamic processes, system modelling, identification and data analysis, linear and nonlinear filtering techniques, fuzzy logic and neural networks for modelling and control, as well as the interaction issues among identification, fault diagnosis, and fault tolerant control. Saverio Farsoni Visiting Assistant Professor, Department of Engineering, University of Ferrara. Saverio Farsoni was born in Mirandola (MO, Italy) in 1987. In 2012 He graduated (cum laude) in Informatics and Automation Engineering at the University of Ferrara with a M. Sc. thesis on simulations in bio-medical environments. Since 2013 he has been PhD student in Engineering Science and, together with his supervisor, Dr. Simani, he works on control systems, fuzzy logic, modelling and identification problems. In particular, his researches deal with fault diagnosis and fault tolerant control for eolic plants, and he published some conference papers about these issues