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Produktbild: Prognostics and Health Management in Energy and Power Systems

Prognostics and Health Management in Energy and Power Systems Integrating Situation Awareness into Large-Scale Foundation Models

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Beschreibung

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

26.01.2026

Verlag

Wiley

Seitenzahl

256

Maße (L/B/H)

18.5/26.4/2.1 cm

Gewicht

612 g

Sprache

Englisch

ISBN

978-1-394-36699-6

Beschreibung

Produktdetails

Einband

Gebundene Ausgabe

Erscheinungsdatum

26.01.2026

Verlag

Wiley

Seitenzahl

256

Maße (L/B/H)

18.5/26.4/2.1 cm

Gewicht

612 g

Sprache

Englisch

ISBN

978-1-394-36699-6

Herstelleradresse

Libri GmbH
Europaallee 1
36244 Bad Hersfeld
DE

Email: gpsr@libri.de

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  • Produktbild: Prognostics and Health Management in Energy and Power Systems

  • List of Figures xi

    List of Tables xvii

    Abstract xix

    About the Authors xxi

    Preface xxiii

    Acknowledgments xxv

    Notations xxvii

    About the Companion Website xxix

    1 Introduction 1

    1.1 The Energy Transition: Toward a Highly Interconnected System of Systems 1

    1.2 The Power Plant and Substation of the Future: Toward Situational Awareness 2

    1.3 The New Paradigm in AI: The Emergence of the Large-scale Foundation Models 3

    1.4 Topics and Organization of the Book 4

    Part I Challenges, Trends, and Asset Management Requirements for the Energy Transition 7

    2 Energy Transition and Digital Transformation 9

    2.1 Introduction 9

    2.2 Digital Transformation 11

    2.3 Energy Transition 12

    2.4 Arrival of DERs 13

    2.5 Lifecycle Requirements, Expectations, and Speed of New Technologies, Introduction in the Electric System 14

    3 Asset Management and Resilience 15

    3.1 Introduction 15

    3.2 Asset Management 15

    3.3 Resilience 17

    3.4 Combining AM and Resilience: Resilience-based AM 18

    3.5 Key Differences Between Reliability and Resilience 20

    3.6 The Link Between DTs, Reliability, LCM, and AM 21

    4 Challenges and Issues Surrounding the Operation of Current and Future Power Plants and Substations 25

    4.1 Introduction 25

    4.2 Reliability and Asset Management 27

    4.3 Different Designs 29

    4.4 Sensor Proliferation 29

    4.5 Dynamic Systems 29

    4.6 Cohabitation of Current and New-generation Technologies 29

    4.7 Software 30

    4.8 Complexity 32

    4.9 Behavioral Nonlinearity of Components and Systems 35

    4.10 System of Systems 35

    4.11 Human Factors 36

    4.12 Data 37

    4.13 Different Operational Time Ranges of the Electric Network 37

    4.14 Possible Multistates of a Component 38

    4.15 Maintenance 38

    4.16 Hidden Failures 39

    4.17 Degradation Process and Obsolescence of Electric and Mechanical Components or Systems 40

    4.18 Climate Change, Extreme Weather Events, and Others 40

    4.19 Complete Life Cycle of Component/System 43

    4.20 Prescriptive Maintenance or Knowledge-based Maintenance 43

    4.21 Regulation Evolution 44

    4.22 Prosumers 45

    4.23 Potential Consequences of Energy Transition 46

    4.24 Remaining Technical Gaps for Electric Power Utilities 47

    Part II Large-scale Foundation Models 51

    5 From Shallow Machine Learning to the Requirements of Large-scale Foundation Models 53

    5.1 Introduction 53

    5.2 ANNs: Theoretical Foundations 54

    5.3 A Brief History of AI: The Main Developments 57

    5.4 Trustworthiness of AI Systems 61

    6 Main Elements of Large-scale Foundation Models: Theoretical Backgrounds 77

    6.1 Introduction 77

    6.2 Modular Learning 78

    6.3 Transformer-based DNNs 82

    6.4 Self-supervised Learning 87

    6.5 Multimodal Fusion 90

    6.6 Multitask Learning 93

    6.7 Graph-oriented Approaches 93

    6.8 Conclusion 99

    7 Main Elements of Large-scale Foundation Models: A Practical and Literature Review 101

    7.1 Introduction 101

    7.2 Transformer Architecture-based Deep Neural Network 101

    7.3 Self-supervised Learning 104

    7.4 Multimodal Fusion 107

    7.5 Multitask Learning 109

    7.6 Graph-oriented Approaches 110

    7.6.1 Anomaly Detection 114

    7.6.2 Diagnostics 114

    7.6.3 Prognostics 114

    7.7 Conclusion and Synthesis 116

    8 Combining Situational Awareness and LSF Models to Support the Energy Transition 119

    8.1 Introduction 119

    8.2 The Target of Future Power Plants and Substations 120

    8.3 What Is the Situational Awareness? 121

    8.4 Incorporating the SA to the Power Plant/Substation of the Future 122

    8.5 Conclusion 124

    9 Toward a New PHM Process 125

    9.1 The Concept of PHM Process 125

    9.2 Integrating ML into the PHM Process 126

    9.3 The Situational Awareness Integrated to the PHM Process 128

    9.4 Conclusion 130

    Part III Industrial Case Study 131

    10 Hydro-generators Prognostics and Health Management 133

    10.1 Introduction 133

    10.2 Description of the Case Study 133

    10.3 Overview of the Global Methodology 142

    11 Set of Deep Learning Models for Feature Extraction 145

    11.1 Introduction 145

    11.2 Feature Extraction from Visual Inspection Data 145

    11.3 Feature Extraction from Text Data 149

    11.4 Feature Extraction from PD 152

    11.5 Conclusion 155

    12 Set of AI-Experts with Deep Modular Learning 157

    12.1 Introduction 157

    12.2 Description of the AI-Experts 158

    12.3 Managing the Mixture-of-AI-Experts 161

    12.4 Experimental Results 164

    12.5 Conclusion 169

    12.6 Appendix 169

    13 Graph-based Approach for Prognostics of Complex Machinery with Sparse Run-to-failure Data 175

    13.1 Introduction 175

    13.2 Preliminaries and Assumptions 176

    13.3 Diagnostics Feature Extraction 177

    13.4 Graph Structure Definition 178

    13.5 Graph Dataset Generation for the Prognostics Considering the Sparse RTF Data 179

    13.6 Assigning a Likelihood for Each Edge 180

    13.7 Graph-based Forecasting Model 181

    13.8 Experimental Results 184

    13.9 Conclusion 190

    Part IV Conclusion 191

    14 Conclusion 193

    14.1 What to Keep in Mind 193

    14.2 Future Directions 195

    Acronyms 199

    Glossary 203

    References 205