Prognostics and Health Management in Energy and Power Systems
Integrating Situation Awareness into Large-Scale Foundation Models
- English
- ePUB (mobile friendly)
- Available on iOS & Android
Prognostics and Health Management in Energy and Power Systems
Integrating Situation Awareness into Large-Scale Foundation Models
About this book
Key insights and practical guidance on transitioning to clean energy while meeting increasing energy demands, covering AI developments and more
Prognostics and Health Management in Energy and Power Systems explores two highly topical subjects, energy transition and the latest advances in Artificial Intelligence, and provides insights and practical guidance for a smooth transition to clean, low-carbon energy while simultaneously continuing to meet the ever-increasing demand for energy.
The first part of this book is completely devoted to the challenges, trends, and Asset Management requirements for the energy transition and explains why the energy system of the future must be resilient, autonomous, anticipatory, and situation-aware. The second part of the book presents key developments in recent years and shows the gradual shift from a collection of monolithic architectures for narrow, singular tasks to a set of modular, reconfigurable architectures capable of handling different types of tasks. An industrial case study is illustrated in the third part of the book, showing that Large-Scale Foundation models represent a promising technique to support the Prognostics and Health Management of the energy system.
This book includes information on:
- Key differences between reliability and resilience, covering Low-Impact, High-Probability events and High-Impact, Low-Frequency events
- Important factors in the operation of current and future power plants and substations, including software, complexity, human error, data, and maintenance
- Modularity, reliability, and explainability of Large-Scale Foundation models
- Transformer-based Deep Neural Networks, covering Attention Mechanisms, Positional Encoding, and input-output data embedding
- Graph-based approaches to prognostics of complex machinery with sparse Run-to-Failure data, covering diagnostics feature extraction and graph dataset generation
Prognostics and Health Management in Energy and Power Systems is an essential forward-thinking reference for engineers and researchers working in the energy sector with an interest in AI techniques and Machine Learning.
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Information
Table of contents
- Cover
- Table of Contents
- Title Page
- Copyright
- List of Figures
- List of Tables
- Abstract
- About the Authors
- Preface
- Acknowledgments
- Notations
- About the Companion Website
- Chapter 1: Introduction
- Part I: Challenges, Trends, and Asset Management Requirements for the Energy Transition
- Part II: Large-scale Foundation Models
- Part III: Industrial Case Study
- Part IV: Conclusion
- Acronyms
- Glossary
- References
- End User License Agreement