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Big Data Application in Power Systems.

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Big Data Application in Power Systems, Second Edition presents a thorough update of the previous volume, providing readers with step-by-step guidance in big data analytics utilization for power system diagnostics, operation, and control. Divided into three parts, this book begins by breaking down th...

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Bibliographic Details
Main Author: Arghandeh, Reza
Corporate Author: ScienceDirect (Online service)
Other Authors: Zhou, Yuxun
Format: eBook
Language:English
Published: San Diego : Elsevier Science & Technology, 2024.
Edition:2nd ed.
Subjects:
Electric power systems.
Big data.
Smart power grids.
Réseaux électriques (Énergie)
Réseaux électriques intelligents.
Electronic books.
Online Access:Connect to the full text of this electronic book
Table of Contents:
  • Intro
  • Big Data Application in Power Systems
  • Copyright
  • Contents
  • About the editors
  • Preface, objective, and overview of the book
  • Acknowledgments for the second edition
  • Section A: Harness the big data from power systems
  • Chapter One: A holistic approach to becoming a data-driven utility
  • 1.1. Introduction
  • 1.2. Aligning internal and external stakeholders
  • 1.3. Taking a holistic approach
  • 1.4. ``Strong ́́first, then ``smarter ́́
  • 1.5. Implementing an ``Observability Strategy ́́
  • 1.6. Increasing visibility with IEDs
  • 1.7. Network response requirements
  • 1.8. Integration before automation
  • 1.9. Functional data paths: Keep it simple
  • 1.10. From sensor to end user: The process
  • 1.11. Customers>consumers>prosumers
  • 1.12. New sources of data: Robotics and UAVs
  • 1.13. Extracting value from data and presenting it
  • 1.14. The transformation
  • 1.15. Three case studies
  • 1.16. Frankfort, Kentucky, and greenfield SCADA, SA
  • 1.17. Unmanned aerial vehicles for vegetation management
  • 1.18. Robotics for substation asset management
  • 1.19. Conclusion
  • 1.20. Looking ahead
  • References
  • Chapter Two: Security and data privacy challenges for data-driven utilities
  • 2.1. Introduction
  • 2.2. Case studies: The state and scope of the threat
  • 2.2.1. Shamoon spearfishing cyberattack at Saudi Aramco
  • 2.2.2. Russian coordinated cyberattack in Ukraine
  • 2.2.3. SolarWinds supply chain hack
  • 2.2.4. The Colonial Pipeline ransomware attack
  • 2.2.5. Impact on practices in the utility industry
  • 2.3. The digitized network increases vulnerability
  • 2.3.1. Attack scenarios
  • 2.4. The role of data analytics
  • 2.4.1. The role of AI in enhancing defending the human attack vector
  • 2.5. Conclusion
  • References
  • Chapter Three: The role of big data and analytics in utilities innovation
  • What we learn in this chapter
  • 3.1. Introduction of big data and analytics as an accelerator of innovation
  • 3.2. Approaches to data-driven innovation
  • 3.3. Integration of renewable energy
  • 3.4. Grid operations
  • 3.5. Cognitive computing on big data
  • 3.6. Weather, the biggest data topic for power systems
  • References
  • Further reading
  • Chapter Four: Big data integration for the digitalization and decarbonization of distribution grids
  • 4.1. Introduction: Challenges toward a net-zero economy
  • 4.2. Grid observability and controllability
  • 4.3. Key drivers of the digital transformation in distribution grids
  • 4.3.1. The role of AI and big data in the distribution network
  • 4.3.2. The role of Digital Twins in the distribution network
  • 4.4. Losses and fault detection
  • 4.5. Conclusions
  • References
  • Further reading
  • Section B: Put the power of big data into power systems
  • Chapter Five: Topology detection in distribution networks with machine learning
  • 5.1. Introduction
  • 5.1.1. Prior work