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Note: Supplemental materials are not guaranteed with Rental or Used book purchases.
Purchase Benefits
What is included with this book?
Understand a new way to model power systems with this comprehensive and practical guide
Graph databases have become one of the essential tools for managing large data systems. Their structure improves over traditional table-based relational databases in that it reconciles more closely to the inherent physics of a power system, enabling it to model the components and the network of a power system in an organic way. The authors’ pioneering research has demonstrated the effectiveness and the potential of graph data management and graph computing to transform power system analysis.
Graph Database and Graph Computing for Power System Analysis presents a comprehensive and accessible introduction to this research and its emerging applications. Programs and applications conventionally modeled for traditional relational databases are reconceived here to incorporate graph computing. The result is a detailed guide which demonstrates the utility and flexibility of this cutting-edge technology.
The book’s readers will also find:
Graph Database and Graph Computing for Power System Analysis is essential for researchers and academics in power systems analysis and energy-related fields, as well as for advanced graduate students looking to understand this particular set of technologies.
Renchang Dai, PhD, is a Consulting Analyst and Project Manager for Puget Sound Energy, Washington, USA. He is a founding member of GE Energy Consluting Smart Grid CoE and an IEEE Senior Member, and has worked and published extensively on graph based power system analysis software.
Guangyi Liu, PhD, is Chief Scientist and Smart Grid CoE at Envision Digital, USA. He is an IEEE Senior member and has extensive experience developing software for graph-based power system analysis across numerous applications.
Preface
Acknowledgements
Section I:
Chapter 1: Introduction
Chapter 2: Graph Database
Chapter 3: Graph Parallel Computing
Chapter 4: Large-Scale Algebraic Equations
Chapter 5: High Dimensional Differential Equations
Chapter 6: Optimization Problems
Chapter 7: Graph-based Machine Learning
Section II:
Chapter 8: Power Systems Modeling
Chapter 9: State Estimation Graph Computing
Chapter 10: Power Flow Graph Computing
Chapter 11: Contingency Analysis Graph Computing
Chapter 12: Economic Dispatch and Unit Commitment
Chapter 13: Automatic Generation Control
Chapter 14: Small-signal Stability
Chapter 15: Transient Stability
Chapter 16: Graph-based Deep Reinforcement Learning on Overload Control
Chapter 17: Conclusions
Appendix
Index
The New copy of this book will include any supplemental materials advertised. Please check the title of the book to determine if it should include any access cards, study guides, lab manuals, CDs, etc.
The Used, Rental and eBook copies of this book are not guaranteed to include any supplemental materials. Typically, only the book itself is included. This is true even if the title states it includes any access cards, study guides, lab manuals, CDs, etc.
