Applications of Deep Machine Learning in Future Energy Systems
- 1st Edition - August 20, 2024
- Imprint: Elsevier
- Editor: Mohammad-Hassan Khooban
- Language: English
- Paperback ISBN:9 7 8 - 0 - 4 4 3 - 2 1 4 3 2 - 5
- eBook ISBN:9 7 8 - 0 - 4 4 3 - 2 1 4 3 1 - 8
Applications of Deep Machine Learning in Future Energy Systems pushes the limits of current Artificial Intelligence techniques to present deep machine learning suitable for the c… Read more
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Request a sales quoteApplications of Deep Machine Learning in Future Energy Systems pushes the limits of current Artificial Intelligence techniques to present deep machine learning suitable for the complexity of sustainable energy systems. The first two chapters take the reader through the latest trends in power engineering and system design and operation before laying out current AI approaches and limitations. Later chapters provide in-depth accounts of specific challenges and the use of innovative third-generation machine learning, including neuromorphic computing, to resolve issues from security to power supply.
An essential tool for the management, control, and modelling of future energy systems, this book maps a practical path towards AI capable of supporting sustainable energy.
- Clarifies the current state and future trends of energy system machine learning and the pitfalls facing our transitioning systems
- Provides guidance on 3rd-generation AI tools for meeting the challenges of modeling and control in modern energy systems
- Includes case studies and practical examples of potential applications to inspire and inform researchers and industry developers
Researchers, research laboratories, practitioners, and Ph.D. students in power systems and electronics
- Cover image
- Title page
- Table of Contents
- Copyright
- List of contributors
- Preface
- Chapter 1. Introduction
- Chapter 2. Artificial intelligence and machine learning in future energy systems (state-of-the-art, future development)
- Abstract
- 2.1 Introduction
- 2.2 Machine learning basics
- 2.3 Machine learning in future energy systems
- 2.4 General observation and future trends
- 2.5 Conclusion
- References
- Chapter 3. Digital twins−assisted design of next-generation DC microgrid
- Abstract
- 3.1 Introduction
- 3.2 Modeling of DCMG
- 3.3 Proposed control
- 3.4 Digital twin−based control strategy for DCMG
- 3.5 Results
- 3.6 Conclusion
- Acknowledgement
- References
- Chapter 4. Intelligent charging station recommendations for electric vehicles in the charging market: a fuzzy−deep learning approach
- Abstract
- 4.1 Introduction
- 4.2 Methodology
- 4.3 FDCR model in the charging market
- 4.4 Supervised learning
- 4.5 FDCR’s decision-making strategy
- 4.6 Score prediction of each EVCS
- 4.7 Simulation results
- 4.8 Conclusion
- Acknowledgment
- References
- Chapter 5. Deep frequency control of power grids under cyber attacks
- Abstract
- 5.1 Introduction
- 5.2 Basics of frequency control in power grids
- 5.3 LFC system vulnerability to cyber attacks
- 5.4 Modeling of the LFC system under cyber attacks
- 5.5 Category of cyber attacks on the LFC system
- 5.6 DoS attack
- 5.7 Time delay attack
- 5.8 FDI attack
- 5.9 Replay attack
- 5.10 Covert attack
- 5.11 Zero dynamic attack
- 5.12 Deep learning−based methods
- 5.13 Conclusion
- References
- Chapter 6. Application of Q-learning in stabilization of multicarrier energy systems
- Abstract
- 6.1 Introduction
- 6.2 Methodologies for modeling
- 6.3 Q-Learning-based ULM controller
- 6.4 Real-time results
- 6.5 Conclusion
- Acknowledgment
- References
- Chapter 7. Design of next-generation of 5G data center power supply based on artificial intelligence
- Abstract
- 7.1 Introduction
- 7.2 Real-time simulation verifications
- 7.3 Conclusion
- Appendix A
- Appendix B
- References
- Chapter 8. Smart EV battery charger based on deep machine learning
- Abstract
- Nomenclature
- 8.1 Introduction
- 8.2 Bus, path, and power requirements
- 8.3 Energy system and its considerations
- 8.4 Energy management
- 8.5 Real-time results
- 8.6 Conclusion and future work
- References
- Chapter 9. Machine learning in talkative power technology
- Abstract
- 9.1 Introduction
- 9.2 Types of TP converter
- 9.3 Applications of TPC
- 9.4 Future perspective of TPC
- 9.5 TP modulations
- 9.6 System modeling and ripple analysis
- 9.7 Machine learning in TP
- 9.8 Conclusion
- References
- Chapter 10. Advanced control of power electronics−based machine learning
- Abstract
- 10.1 Introduction and preliminaries
- 10.2 Dynamics of DC/DC buck converter supplying CPLs
- 10.3 Sensorless backstepping controller
- 10.4 SAC-based parameter tuner for sensorless controller design
- 10.5 Real-time simulation verifications
- 10.6 Conclusion
- References
- Chapter 11. Multilevel energy management and optimal control system in smart cities based on deep machine learning
- Abstract
- 11.1 Introduction
- 11.2 IEMOCS topology and three-level model in INMG structure
- 11.3 IEMOCS structure based on hybrid TSKFS&MADRL model
- 11.4 Simulation results
- 11.5 Conclusion
- Appendix 1
- References
- Index
- Edition: 1
- Published: August 20, 2024
- Imprint: Elsevier
- No. of pages: 334
- Language: English
- Paperback ISBN: 9780443214325
- eBook ISBN: 9780443214318
MK
Mohammad-Hassan Khooban
Dr. Mohammad-Hassan Khooban is an Assistant Professor in the Department of Engineering and the Director of the Power Circuits and Systems Research Group at Aarhus University in Denmark. He has authored or co-authored more than 220 publications in peer-reviewed journals (mostly IEEE) and international conferences, written three book chapters, and holds one patent. He has been involved in six national and international projects. He was identified in 2019, 2020, and 2021 by Stanford University as one of the world’s top 2% researchers in engineering. He was also ranked 16th in the list of top 30 Electronics and Electrical Engineering Scientists in Denmark in 2022. His research interests include the application of advanced control, and optimization of artificial intelligence-inspired techniques in power electronics and systems.
Affiliations and expertise
Department of Engineering - Cyper-Physical Systems, Aarhus University, Aarhus N, DenmarkRead Applications of Deep Machine Learning in Future Energy Systems on ScienceDirect