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E-grāmata: Fault Diagnosis and Sustainable Control of Wind Turbines: Robust Data-Driven and Model-Based Strategies

(Assistant Professor, Department of Engineering, University of Ferrara), (Visiting Assistant Professor, Department of Engineering, University of Ferrara)
  • Formāts: PDF+DRM
  • Izdošanas datums: 02-Jan-2018
  • Izdevniecība: Butterworth-Heinemann Inc
  • Valoda: eng
  • ISBN-13: 9780128129852
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Fault Diagnosis and Sustainable Control of Wind Turbines: Robust Data-Driven and Model-Based StrategiesPalielināt
  • Formāts: PDF+DRM
  • Izdošanas datums: 02-Jan-2018
  • Izdevniecība: Butterworth-Heinemann Inc
  • Valoda: eng
  • ISBN-13: 9780128129852
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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.

  • 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
Foreword ix
Preface xi
Glossary xiii
1 Introduction
1.1 Introduction
1(1)
1.2 Motivations
2(1)
1.3 Nomenclature
2(1)
1.4 Introduction to Wind Turbine Modeling
3(3)
1.5 Introduction to Fault Diagnosis Methods
6(2)
1.6 Introduction to Fault Tolerant Control Methods
8(2)
1.7 Modeling and Advanced Control Benchmarking
10(2)
1.8 Outline of the Monograph
12(1)
1.9 Summary
12(1)
2 System and Fault Modeling
2.1 Introduction
13(1)
2.2 System Description
13(2)
2.2.1 Wind Turbine Categories
13(2)
2.3 Wind Turbine Main Components
15(9)
2.3.1 Aerodynamic System
16(1)
2.3.2 Drive-Train Model
16(2)
2.3.3 Load Carrying Structure and Blade Models
18(1)
2.3.4 Power System Model
19(1)
2.3.5 Pitch System Model
19(1)
2.3.6 Wind Model
20(1)
2.3.7 Model-Reality Mismatch
21(2)
2.3.8 Actuator and Sensor Models
23(1)
2.3.9 Overall Model Structure
23(1)
2.4 Wind Turbine Control Issues
24(5)
2.4.1 Advanced Control Solutions
24(3)
2.4.2 Wind Turbines Feedback Control
27(1)
2.4.3 Structural and Drive-Train Stress Damper
28(1)
2.4.4 Bumpless Transfer
28(1)
2.5 Wind Turbine Benchmark
29(4)
2.5.1 Wind Turbine Benchmark Model
29(2)
2.5.2 Wind Turbine Controller Model
31(1)
2.5.3 The Measurement Model
32(1)
2.5.4 Wind Turbine Fault Scenario
32(1)
2.5.5 Model Parameters
33(1)
2.5.6 Wind Turbine Benchmark Overall Model
33(1)
2.6 Wind Farm Benchmark
33(3)
2.6.1 Wind and Wake Model
34(1)
2.6.2 Wind Farm Benchmark Overall Model
35(1)
2.6.3 Wind Farm Fault Scenario
35(1)
2.6.4 Model Parameters
36(1)
2.7 Fault Analysis
36(5)
2.7.1 Failure Mode and Effect Analysis
38(1)
2.7.2 Fault Specifications and Requirements
39(2)
2.8 Summary
41(2)
3 Fault Diagnosis for Wind Turbine Systems
3.1 Introduction
43(6)
3.1.1 Plant and Fault Models
43(3)
3.1.2 Residual Generation General Scheme
46(2)
3.1.3 Residual Evaluation for Change Detection
48(1)
3.2 Residual Generation Model-Based Approaches
49(9)
3.2.1 Parity Space Methods
49(2)
3.2.2 Observer-Based Methods
51(3)
3.2.3 Filtering Methods
54(2)
3.2.4 Nonlinear Geometric Approach Method to FDI
56(2)
3.3 Residual Generation Data-Driven Approaches
58(13)
3.3.1 Recursive Identification Approaches
58(2)
3.3.2 Artificial Intelligence Methods
60(7)
3.3.3 Fault Diagnosis Technique Integration
67(4)
3.4 Robust Residual Generation Issues
71(4)
3.5 Summary
75(2)
4 Fault Tolerant Control for Wind Turbine Systems
4.1 Introduction
77(10)
4.1.1 Integration of Fault Diagnosis and Control
79(1)
4.1.2 Nonlinear Adaptive Filters for Fault Estimation
80(7)
4.2 Wind Turbine Control Strategies
87(10)
4.2.1 Fuzzy Modeling for Control
88(2)
4.2.2 Recursive Identification for Adaptive Control
90(6)
4.2.3 Sustainable Control
96(1)
4.3 Fault Tolerant Control Architectures
97(3)
4.3.1 Controller Compensation and Active Fault Tolerance
98(2)
4.4 Fault Tolerant Control Oriented Fault Diagnosis
100(4)
4.4.1 Fault Tolerant Control for Wind Turbine Systems
103(1)
4.5 Summary
104(1)
5 Application Results
5.1 Introduction
105(1)
5.2 Wind Turbine Model Application
105(12)
5.2.1 Data-Driven Fault Diagnosis Examples
107(3)
5.2.2 Model-Based Fault Diagnosis Examples
110(2)
5.2.3 Fault Diagnosis Comparative Results
112(2)
5.2.4 Performance and Robustness Analysis
114(3)
5.3 Advanced Control Designs for Wind Turbines
117(18)
5.3.1 Sustainable Control Design
124(1)
5.3.2 Data-Driven Fault Tolerant Control Examples
125(4)
5.3.3 Model-Based Fault Tolerant Control Examples
129(4)
5.3.4 Performance Evaluation and Robustness Analysis
133(1)
5.3.5 Comparative Results and Stability Analysis
134(1)
5.4 Wind Farm Model Application
135(11)
5.4.1 Control Design for Wind Farm
137(2)
5.4.2 Data-Driven Fault Diagnosis
139(2)
5.4.3 Model-Based Fault Diagnosis
141(1)
5.4.4 Comparative and Robustness Analysis
142(1)
5.4.5 Sustainable Control for the Wind Farm Simulator
143(3)
5.5 Summary
146(1)
6 Matlab and Simulink Implementations
6.1 Introduction
147(1)
6.2 Wind Turbine System Benchmark
147(15)
6.2.1 Wind Turbine Simulator Main Components
148(1)
6.2.2 Aerodynamic Block
148(2)
6.2.3 Drive-Train Block
150(1)
6.2.4 Power System Block
151(1)
6.2.5 Pitch System Block
151(1)
6.2.6 Wind Model Block
152(2)
6.2.7 Actuator and Sensor Model Block
154(1)
6.2.8 Wind Turbine Controller Block
154(3)
6.2.9 Wind Turbine Fault Blocks
157(2)
6.2.10 Wind Turbine Model Parameter Initialization
159(3)
6.3 Wind Farm System Benchmark
162(18)
6.3.1 Wind and Wake Block
162(1)
6.3.2 Wind Farm Fault Block
163(1)
6.3.3 Wind Farm Model Parameter Initialization
163(6)
6.3.4 Fault Diagnosis Module Implementation
169(3)
6.3.5 Fault Tolerant Control Module Implementation
172(6)
6.3.6 Monte Carlo Simulation Tool
178(1)
6.3.7 Hardware-ln-The-Loop Tests
178(2)
6.4 Summary
180(3)
7 Conclusions
7.1 Introduction
183(1)
7.2 Closing Remarks
184(5)
7.3 Further Work and Open Problems
189(11)
7.3.1 Sustainable Control Design Objectives
190(2)
7.3.2 Sustainable Control Concepts and Approaches
192(1)
7.3.3 Sustainable Control Approaches and Working Methods
193(2)
7.3.4 Sustainable Control Design Ambition
195(3)
7.3.5 Sustainable Control Innovation Potentials
198(1)
7.3.6 Sustainable Control Expected Impacts
199(1)
7.4 Summary
200(1)
References 201(10)
Index 211
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 books 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 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 biomedical 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
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