| Edition |
2nd ed. |
| Description |
1 online resource (450 p.) |
| Note |
Description based upon print version of record. |
| 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 |
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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 |
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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 |
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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 |
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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 |
| Note |
5.1.2. Technical contribution |
| Summary |
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 the big picture for electric utilities before zooming in to examine theoretical problems and solutions in detail. Finally, the third section provides case studies and applications, demonstrating solution troubleshooting and design from a variety of perspectives and for a range of technologies. Bringing back a team of global experts and drawing on fresh, emerging perspectives, this book provides cutting-edge advice for meeting today's challenges in this rapidly accelerating area of power engineering. Readers will develop new strategies and techniques for leveraging data towards real-world outcomes. |
| Subject |
Electric power systems.
|
|
Big data.
|
|
Smart power grids.
|
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Réseaux électriques (Énergie)
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Réseaux électriques intelligents.
|
| Added Author |
Zhou, Yuxun.
|
| Other Form: |
Print version: Arghandeh, Reza Big Data Application in Power Systems San Diego : Elsevier Science & Technology,c2024 9780443215247 |
| ISBN |
9780443219511 |
|
0443219516 |
| Standard No. |
AU@ 000077515033 |
|
