Smart Energy and Electric Power Systems
Current Trends and New Intelligent Perspectives
- 1st Edition - September 17, 2022
- Imprint: Elsevier
- Editors: Sanjeevikumar Padmanaban, Jens Bo Holm-Nielsen, Kayal Padmanandam, Rajesh Kumar Dhanaraj, Balamurugan Balusamy
- Language: English
- Paperback ISBN:9 7 8 - 0 - 3 2 3 - 9 1 6 6 4 - 6
- eBook ISBN:9 7 8 - 0 - 3 2 3 - 9 1 6 8 5 - 1
Smart Energy and Electric Power Systems: Current Trends and New Intelligent Perspectives reviews key applications of intelligent algorithms and machine learning techniques to… Read more
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Request a sales quoteSmart Energy and Electric Power Systems: Current Trends and New Intelligent Perspectives reviews key applications of intelligent algorithms and machine learning techniques to increasingly complex and data-driven power systems with distributed energy resources to enable evidence-driven decision-making and mitigate catastrophic power shortages. The book reviews foundations towards the integration of machine learning and smart power systems before addressing key challenges and issues. The work then explores AI- and ML-informed techniques to rebalancing of supply and demand. Methods discussed include distributed energy resources and prosumer markets, electricity demand prediction, component fault detection, and load balancing.
Security solutions are introduced, along with potential solutions to cyberattacks, security data detection and critical loads in power systems. The work closes with a lengthy discussion, informed by case studies, on integrating AI and ML into the modern energy sector.
- Helps improve the prediction capability of AI algorithms to make evidence-based decisions in the smart supply of electricity, including load shedding
- Focuses on how to integrate AI and ML into the energy sector in the real-world, with many chapters accompanied by case studies
- Addresses a number of proven AI and ML- informed techniques in rebalancing supply and demand
Early career researchers focusing on machine learning, energy grid applications, and system design for smart grids; Professionals in the energy sector, researchers and application developers of machine learning and artificial intelligence-based smart grid solutions
- Cover image
- Title page
- Table of Contents
- Copyright
- List of contributors
- Preface
- Chapter 1. Smart power systems: an eyeview
- Abstract
- 1.1 Introduction to artificial intelligence
- 1.2 Necessity of artificial intelligence in power systems
- 1.3 Modern artificial intelligence techniques in power system
- 1.4 Artificial intelligence techniques in smart grids
- 1.5 Challenges in smartgrid
- References
- Chapter 2. Smart energy and electric power system: current trends and new intelligent perspectives and introduction to AI and power system
- Abstract
- 2.1 Introduction
- 2.2 Power system
- 2.3 Overviews of artificial intelligence
- 2.4 Applications of artificial intelligence
- 2.5 Types of machine learning
- 2.6 Machine learning methods
- 2.7 Deep learning and its types
- 2.8 Deep learning models
- 2.9 Deep neural networks
- 2.10 Artificial neural networks
- 2.11 Convolution neural network
- 2.12 Fuzzy system
- 2.13 Expert systems in power system
- 2.14 Expert systems in power systems
- 2.15 Genetic algorithms in power systems
- 2.16 Conclusion
- References
- Chapter 3. Recent developments of smart energy networks and challenges
- Abstract
- 3.1 Introduction
- 3.2 Smart energy
- 3.3 Challenges in smart energy
- 3.4 Conclusion
- References
- Chapter 4. A smart and efficient IoT-AI and ML-based multifunctional system for multilevel power distribution management
- Abstract
- 4.1 Introduction
- 4.2 Integration of Internet of things-artificial intelligence and machine learning toward power distribution and health monitoring systems
- 4.3 Grid-assisted technology
- 4.4 Transformer health monitoring from traditional till modern techniques
- 4.5 Forecast models toward consumer demand and stock analysis
- 4.6 Forecast models toward consumer demand and stock analysis
- 4.7 Transformer health monitoring from traditional till modern techniques
- 4.8 Conclusion
- References
- Chapter 5. A survey on AI- and ML-based demand forecast analysis of power using IoT-based SCADA
- Abstract
- 5.1 Introduction
- 5.2 SCADA-based power consumption monitoring and metering systems
- 5.3 Traditional power demand prediction method
- 5.4 Machine learning-based demand forecast analyst model
- 5.5 Deep learning-based demand forecast analytic model
- 5.6 Summary
- 5.7 Conclusion
- References
- Further reading
- Chapter 6. Impact of artificial intelligence techniques in distributed smart grid monitoring system
- Abstract
- 6.1 Introduction
- 6.2 Future energy system
- 6.3 Distributed smart grid monitoring system using artificial intelligence
- 6.4 Artificial intelligence techniques for the integration of renewable energy system
- 6.5 Artificial intelligence techniques for integration of energy storage system
- 6.6 Economic feature and market deregulation in smart grid (current trends)
- 6.7 Challenges and issues for implementation of artificial intelligence-based distributed smart grid monitoring system
- 6.8 Conclusion
- References
- Chapter 7. Smart power quality control measures
- Abstract
- 7.1 Introduction
- 7.2 Cause of reduced power eminence
- 7.3 Consequence of poor power quality
- 7.4 Future: opportunities and regulatory problems
- 7.5 Conclusion
- References
- Chapter 8. Directional overcurrent relay coordination optimization
- Abstract
- 8.1 Introduction
- 8.2 Formulation of the coordination optimization problem
- 8.3 The improved ant colony optimization
- 8.4 Improved ant colony optimization for protection coordination
- 8.5 Improved differential evolution for protection coordination
- 8.6 Results performance study among genetic algorithm, IACO and IDE
- 8.7 Summary
- Acknowledgments
- Conflict of interest
- References
- Chapter 9. Monitoring of wind power control in microgrid
- Abstract
- 9.1 Introduction
- 9.2 Problem statement
- 9.3 Methodology
- 9.4 Implementation
- 9.5 Simulation results
- 9.6 Conclusion
- 9.7 Future scope
- Conflict of interest
- References
- Chapter 10. Cyber attacks, security data detection, and critical loads in the power systems
- Abstract
- 10.1 Introduction
- 10.2 Cyber attacks in smart grids
- 10.3 Smart grid cyber security needs and standards
- 10.4 Proposed security solutions for smart grids
- References
- Chapter 11. Blockchain-based secured payment in IoE
- Abstract
- 11.1 Introduction
- 11.2 Blockchain
- 11.3 Internet of everything (IoE)
- 11.4 Secure payments in IoE
- References
- Index
- Edition: 1
- Published: September 17, 2022
- No. of pages (Paperback): 226
- No. of pages (eBook): 226
- Imprint: Elsevier
- Language: English
- Paperback ISBN: 9780323916646
- eBook ISBN: 9780323916851
SP
Sanjeevikumar Padmanaban
Sanjeevikumar Padmanaban is a Full Professor in Electrical Power Engineering with the Department of Electrical Engineering, Information Technology, and Cybernetics of the University of South-Eastern Norway, Norway. He has over a decade of academic and teaching experience, including Associate/Assistant Professorships at the University of Johannesburg, South Africa (2016-2018), Aalborg University, Denmark (2018-2021) and the CTIF Global Capsule Laboratory at Aarhus University, Denmark (2021-present). Prof. Padmanaban received a lifetime achievement award from Marquis Who’s Who - USA 2017 for contributing to power electronics and renewable energy research, and was listed among the world’s top 2% of scientists by Stanford University, USA in 2019.
Affiliations and expertise
Professor, Department of Electrical Engineering, Information Technology, and Cybernetics, University of South-Eastern Norway, NorwayJH
Jens Bo Holm-Nielsen
Dr Jens Bo Holm-Nielsen is Associate Professor and Head of Center for Bioenergy and Green Engineering, Aalborg University, Aalborg, Denmark
Affiliations and expertise
Associate Professor and Head, Center for Bioenergy and Green Engineering, Aalborg University, Aalborg, DenmarkKP
Kayal Padmanandam
Dr.Kayal Padmanandam has over a decade of credentials in the domain of Computer Science with wide exposure through teaching, research, and industry. She is passionate about research and specialized in Data Science and Machine Learning Algorithms in which she pursued her doctoral research. She has several publications especially related to machine-learning applications. She is a post-graduate/graduate educator for Engineering and Science scholars. Currently, she is working as an Associate Professor in the Department of Information Technology and as a Member of the Research & Development Cell, BVRITH College of Engineering, Hyderabad, India.
Affiliations and expertise
Associate Professor, Department of Information Technology and Member of the Research and Development Cell, BVRITH College of Engineering, Hyderabad, IndiaRD
Rajesh Kumar Dhanaraj
Dr. Rajesh Kumar Dhanaraj is a professor at the Symbiosis International (Deemed University) in Pune, India. His research and publication interests include cyber-physical systems, wireless sensor networks, and cloud computing. He is a senior member of the Institute of Electrical and Electronics Engineers (IEEE), a member of the Computer Science Teacher Association (CSTA) and member of the International Association of Engineers (IAENG). He is an expert advisory panel member of Texas Instruments Inc. (USA), and an associate editor of International Journal of Pervasive Computing and Communications (Emerald Publishing).
Affiliations and expertise
Professor, Symbiosis International (Deemed University), Pune, IndiaBB
Balamurugan Balusamy
Dr. Balamurugan Balusamy is currently working as an Associate Dean Student in Shiv Nadar Institution of Eminence, Delhi-NCR. He is part of the Top 2% Scientists Worldwide 2023 by Stanford University in the area of Data Science/AI/ML. He is also an Adjunct Professor, Department of Computer Science and Information Engineering, Taylor University, Malaysia. His contributions focus on engineering education, block chain, and data sciences.
Affiliations and expertise
Shiv Nadar University, Delhi-NCR, IndiaRead Smart Energy and Electric Power Systems on ScienceDirect