Non-destructive testing and condition monitoring techniques for renewable energy industrial assets

Non-Destructive Testing and Condition Monitoring Techniques for Renewable Energy Industrial Assets integrates state-of-the-art information and discusses future developments and their significance to the improvement of the renewable energy industry. Renewable energy assets are complex systems with se...

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Bibliographic Details
Other Authors Papaelias, Mayorkinos (Editor), Garcia, Fausto Pedro (Editor), Karyotakis, Alexander (Editor)
Format Electronic eBook
LanguageEnglish
Published Kidlington, Oxford : Butterworth-Heinemann, an imprint of Elsevier, 2020.
Subjects
Energy industries.
Renewable energy sources.
Nondestructive testing.
elektronické knihy
electronic books
Online AccessFull text
ISBN9780128097472
0128097477
9780081010945
008101094X
Physical Description1 online resource

Cover

Table of Contents:
  • Front Cover
  • Non-Destructive Testing and Condition Monitoring Techniques for Renewable Energy Industrial Assets
  • Copyright
  • Contents
  • Contributors
  • Introduction to non-destructive testing and condition monitoring techniques for renewable energy industrial assets
  • Chapter 1: Wind turbines: A general reliability analysis
  • 1 Introduction
  • 2 Wind turbine components
  • 3 Reliability analysis
  • 3.1 Fault tree analysis
  • 3.2 Binary decision diagrams
  • 3.3 Conversion from FTA to BDD
  • 3.4 Rankings for events
  • 4 Experiments
  • 4.1 Foundation and tower
  • 4.2 Blades
  • 4.3 Generator, electrical and electronic components
  • 4.4 Power train
  • 5 Conclusions
  • Acknowledgements
  • References
  • Chapter 2: Wind turbine inspection and condition monitoring
  • 1 Introduction
  • 2 Avoiding catastrophic failure
  • 3 Inspection of wind turbine components
  • References
  • Chapter 3: An overview of wind turbine maintenance management
  • 1 Introduction
  • 2 Condition monitoring and the SCADA system
  • 3 Maintenance model approaches
  • 3.1 Statistical methods
  • 3.2 Trend analysis
  • 3.3 Filtering methods
  • 3.4 Time-domain analysis
  • 3.5 Cepstrum analysis
  • 3.6 Time synchronous averaging (TSA)
  • 3.7 Fast-Fourier transform
  • 3.8 Amplitude demodulation (acceleration enveloping)
  • 3.9 Order analysis
  • 3.10 Wavelet transform
  • 3.11 Hidden Markov models
  • 3.12 Novel techniques: Expert systems and artificial intelligent
  • 4 Fault tree analysis
  • 5 Conclusions
  • Acknowledgements
  • References
  • Chapter 4: Non-destructive testing for the evaluation of icing blades in wind turbines
  • 1 Introduction
  • 2 Icing conditions
  • 3 Methods for detecting ice on a wind turbine
  • 3.1 Detection methods
  • 3.2 Damping of ultrasonic waves
  • 3.3 Measurement of frequency in resonance.
  • 3.4 Vibration measurement of a diaphragm
  • 3.5 Optical measurement techniques
  • 3.5.1 Measurement of the reflected light
  • 3.5.2 Infrared spectroscopy
  • 3.6 Measurement of ice loads
  • 3.7 Heated anemometers
  • 3.8 Prediction methods or probability frost maps
  • 3.9 Other prediction systems
  • 3.9.1 Change of electrical properties
  • 4 Methods of prevention for the occurrence of ice on a wind turbine
  • 4.1 Heating of blades
  • 4.2 Ice-repellent coating
  • 4.3 Microwaves
  • 5 Methods for removing ice from a wind turbine blade
  • 5.1 Mechanical operations
  • 5.2 Black paint
  • 5.3 Other methods
  • 6 Detection and removal of ice systems
  • 6.1 ENERCON detection system
  • 6.2 LID-3300IP ice detector sensor designed by Labkotech
  • 6.3 0871LH1 Goodrich freezing rain sensor
  • 6.4 The ice load surveillance sensor ice monitor
  • 6.5 Ice Meister model 9734
  • 6.6 HoloOptics series T40 sensor
  • 6.7 WXT-520 sensor
  • 6.8 WAA252 heated anemometer sensor
  • 6.9 NRG IceFree 3
  • 6.10 ENERCON melting system
  • 6.11 Blades heating
  • 7 Conclusion
  • Acknowledgements
  • References
  • Chapter 5: Wind turbine gearboxes: Failures, surface treatments and condition monitoring
  • 1 Introduction-Wind turbine gearboxes
  • 2 Gearbox failures
  • 3 Gearbox condition monitoring
  • 3.1 Vibration analysis
  • 3.1.1 Time domain analysis
  • 3.1.2 Frequency domain analysis
  • 3.2 Acoustic emission monitoring
  • 3.3 Oil analysis
  • 4 Surface engineering
  • 4.1 Carburising
  • 4.2 Nitriding
  • 4.3 Duplex and composite coatings
  • References
  • Further reading
  • Chapter 6: Non-destructive testing of wind turbines using ultrasonic waves
  • 1 Introduction
  • 2 Case study
  • 3 Approach
  • 4 Pattern recognition by neural network (multilayer perceptron)
  • 5 Results
  • 6 Conclusion
  • Acknowledgement
  • References.
  • Chapter 7: A review on condition monitoring system for solar plants based on thermography
  • 1 Introduction
  • 2 Fundaments of thermography
  • 2.1 Techniques
  • 3 Thermography for solar panels
  • 3.1 Active solar thermography
  • 3.2 Passive solar thermography
  • 4 Advantages and disadvantages of solar panel thermography
  • 4.1 Advantages
  • 4.2 Disadvantages
  • 5 Factors that affect the measurement
  • 6 Principal failures studied
  • 7 Conclusion
  • Acknowledgements
  • References
  • Chapter 8: Remotely operated vehicle applications
  • 1 Introduction
  • 2 Non-destructive testing in wind turbines
  • 3 Description of the system
  • 3.1 Robot platform
  • 3.2 Vacuum system
  • 4 System applications
  • 5 Conclusion
  • Acknowledgements
  • References
  • Chapter 9: Remote condition monitoring for photovoltaic systems
  • 1 Introduction
  • 2 Solar panels
  • 3 Experimental platform
  • 3.1 Hardware
  • 3.2 Software
  • 4 Case study
  • 5 Results
  • 6 Conclusion
  • Acknowledgements
  • References
  • Chapter 10: Tidal turbines
  • 1 Overview
  • 1.1 Early tidal turbines
  • 1.2 Modern tidal power generators
  • 1.3 Rotor blades
  • 1.4 Drive train
  • 1.5 Gearbox
  • 1.6 Generator
  • 1.7 Power converter
  • 1.8 Low-voltage consumed power
  • 1.9 Control and management systems
  • 1.10 Supporting structure
  • 1.11 Power transmission
  • References
  • Further reading
  • Chapter 11: Automatic statistical analysis of acoustic emission data sets
  • 1 Introduction
  • 2 Automated data clustering algorithms
  • 2.1 The k-means algorithm
  • 2.2 The Forgy algorithm
  • 2.3 The ISODATA algorithm
  • 2.4 The SOM-LVQ algorithm
  • 2.5 The Fast Fourier Transform
  • 3 Conclusion
  • References
  • Chapter 12: Non-destructive methods for detection and localisation of partial discharges
  • 1 Introduction
  • 2 Experimental setup.
  • 2.1 Measuring equipment of damper alternating current (DAC)
  • 3 Case studies
  • 3.1 Case 1
  • 3.2 Case 2
  • 4 Results
  • 4.1 Case 1
  • 4.2 Case 2
  • 5 Conclusion
  • Acknowledgements
  • References
  • Further reading
  • Index
  • Back Cover.

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