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Fault Diagnosis and Sustainable Control of Wind Turbines

Robust Data-Driven and Model-Based Strategies

  • 1st Edition - January 2, 2018
  • Latest edition
  • Authors: Silvio Simani, Saverio Farsoni
  • Language: English

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… Read more

Description

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.

Key features

  • Provides wind turbine models with varying complexity, as well as the solutions proposed and developed by the authors
  • Addresses in detail the design, development and realistic implementation of fault diagnosis and fault tolerant control strategies for wind turbine systems
  • Addresses the development of sustainable control solutions that, in general, do not require the introduction of further or redundant measurements
  • Proposes active fault tolerant ('sustainable') solutions that are able to maintain the wind turbine working conditions with gracefully degraded performance before required maintenance can occur
  • Presents full coverage of the diagnosis and fault tolerant control problem, starting from the modeling and identification and finishing with diagnosis and fault tolerant control approaches
  • Provides MATLAB and Simulink codes for the solutions proposed

Readership

Mechanical, Electrical and Power engineers working in industry and researchers in academic and scientific institutions wishing to gain insight into the applications potential of new fault diagnosis and sustainable control methods

Table of contents

Chapter 1 Introduction1.1 Motivations1.2 Nomenclature1.3 Fault Diagnosis Methods1.4 Fault Tolerant Control Methods1.5 Outline of the MonographChapter 2 System and Fault Modelling2.1 System Description2.1.1 Wind Turbine Categories2.1.2 Main Components of Wind Turbines2.1.3 The Overall Wind Turbine Analytical Description2.1.4 Wind Turbine Control Issues2.2 The Wind Turbine Benchmark System2.2.1 The Turbine Model2.2.2 The Controller Model2.2.3 The Measurement Model2.2.4 The Fault Scenarios2.2.5 Model Parameters2.2.6 The Complete Model2.3 The Wind Farm Benchmark System2.3.1 The Wind and Wake Model2.3.2 The Plant Model2.3.3 The Fault Scenarios2.3.4 Model ParametersChapter 3 Data–Driven Modelling and Identification3.1 Fuzzy Modelling and Identification3.1.1 Introduction to Fuzzy Logic3.1.2 Takagi–Sugeno Fuzzy Rules3.1.3 FIS Design from Data3.2 Neural Network Modelling3.2.1 Introduction to Neural Network3.2.2 Neural Network Architectures3.2.3 Training the Network3.2.4 Other Training Algorithms3.2.5 Problems with Neural NetworksChapter 4 Fault Diagnosis and Fault Tolerant Control Schemes4.1 Failure Mode & Effect Analysis4.2 Fault Diagnosis4.3 Fault Tolerant ControlChapter 5 Nonlinear Geometric Approach for Fault Diagnosis5.1 NLGA FDI Scheme Design5.2 NLGA Robustness Improvements5.2.1 Filter and Observer Residual Function Forms5.2.2 NLGA Residual Optimisation5.3 NLGA Adaptive Filter Fault Estimation5.3.1 Adaptive Filtering Algorithm5.3.2 Disturbance Distribution EstimationChapter 6 Simulations, Experiments and Results6.1 Wind Turbine Simulations6.1.1 Fault Diagnosis via Fuzzy Identified Models6.1.2 Fault Diagnosis via Neural Networks6.1.3 Validation and Comparative Analysis6.1.4 Fault Tolerant Control6.2 Wind Farm Simulations6.2.1 Fault Diagnosis6.2.2 Comparative Analysis6.2.3 Fault Tolerant Control6.3 Hardware in the Loop Tests6.4 Wind Farm NLGA AFTCChapter 7 Conclusions7.1 Concluding Remarks7.2 Summary7.3 Further WorksReferences

Product details

  • Edition: 1
  • Latest edition
  • Published: January 4, 2018
  • Language: English

About the authors

SS

Silvio Simani

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.
Affiliations and expertise
Assistant Professor, Department of Engineering, University of Ferrara

SF

Saverio Farsoni

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
Affiliations and expertise
Visiting Assistant Professor, Department of Engineering, University of Ferrara

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