Advanced Data Analytics for Power Systems
Published on 30. March 2022
978-1-108-84938-8 (ISBN)
Description
Experts in data analytics and power engineering present techniques addressing the needs of modern power systems, covering theory and applications related to power system reliability, efficiency, and security. With topics spanning large-scale and distributed optimization, statistical learning, big data analytics, graph theory, and game theory, this is an essential resource for graduate students and researchers in academia and industry with backgrounds in power systems engineering, applied mathematics, and computer science.
Reviews / Votes
'There are only a few industries that generate an equally large amount of data with a comparable variety, and societal importance. Data analytics is thus rightfully at the heart of modern power systems operations and planning. Focusing on applications in power systems, this book gives an excellent account of recent developments and of the broad range of algorithms and tools in the area of data analytics, as well as of the applications of these tools for solving challenging problems from a novel angle. Covering a wide range of fundamental problems, from state estimation to load scheduling and anomaly detection, the book is not only an excellent source of inspiration, but can also serve as an extensive reference for the gamut of operational problems faced in the power industry.' Gyoergy Dan, KTH Royal Institute of Technology 'The editors have brought together leading researchers at the intersection of data analytics and power systems to provide us with an authoritative reference that is comprehensive, coherent and timely. It treats classical topics such as state estimation, optimal power flow, and anomaly identification, as well as emerging topics such as phase measurement unit data recovery and privacy, probabilistic price forecasting, and distributed load management. It introduces a wide array of modern techniques to power system analysis from sparse representation, graph signal processing, distributed and feedback optimization, statistics and random matrix theory, deep learning, and mean field games. A useful reference for students, researchers, and practitioners.' Steven Low, CaltechMore details
Language
English
Place of publication
Cambridge
United Kingdom
Target group
Professional and scholarly
College/higher education
Illustrations
Worked examples or Exercises
File size
18,42 MB
ISBN-13
978-1-108-84938-8 (9781108849388)
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Schweitzer Classification
Other editions
Additional editions
Ali Tajer | Samir M. Perlaza | H. Vincent Poor
Advanced Data Analytics for Power Systems
Book
04/2021
Cambridge University Press
€132.50
Shipment within 15-20 days
Persons
Content
Introduction; Preface Ali Tajer, Samir M. Perlaza and H. Vincent Poor; 1. Learning power grid topologies Guido Cavraro, Vassilis Kekatos, Liang Zhang and Georgios B. Giannakis; 2. Probabilistic forecasting of power system and market operations Yuting Ji, Lang Tong and Weisi Deng; 3. Deep learning in power systems Yue Zhao and Baosen Zhang; 4. Estimating the system state and network model errors Ali Abur, Murat Gol and Yuzhang Lin; 5. Quickest detection and isolation of tranmission line outages Venugopal V. Veeravalli and Alejandro Dominguez-Garcia; 6. Active sensing for quickest anomaly detection Ali Tajer and Javad Heydari; 7. Random matrix theory for analyzing spatio-temporal data Robert Qiu, Xing He, Lei Chu and Xin Shi; 8. Graph-theoretic analysis of power grid robustness Dorcas Ofori-Boateng, Asim Kumer Dey, Yulia R. Gel and H. Vincent Poor; 9. Bayesian attacks Inaki Esnaola, Samir M Perlaza and Ke Sun; 10. Smart meter data privacy Giulio Giaconia, Deniz Gunduz and H. Vincent Poor; 11. Data quality and privacy enhancement Meng Wang and Joe H Chow; 12. Frequency estimation using voltage phasor angles revisited Danilo P. Mandic, Sithan Kanna, Yili Xia and Anthony G. Constantinides; 13. Graph signal processing for the power grid Anna Scaglione, Raksha Ramakrishna and Mahdi Jamei; 14. A sparse representation approach for anomaly identification Hao Zhu and Chen Chen; 15. Uncertainty-aware power systems operation Daniel Bienstock; 16. Distributed optimization for power and energy systems Emiliano Dall'Anese and Nikolaos Gatsis; 17. Distributed load management Changhong Zhao, Vijay Gupta and Ufuk Topcu; 18. Analytical models for emerging energy storage applications I. Safak Bayram and Michael Devetsikiotis; 19. Distributed power consumption scheduling Samson Lasaulce, Olivier Beaude and Mauricio Gonz?alez; 20. Electric vehicles and mean-field Dario Bauso and Toru Namerikawa; 21. Prosumer behaviour: decision making with bounded horizon Mohsen Rajabpour, Arnold Glass, Robert Mulligan and Narayan B. Mandayam; 22. Storage allocation for price volatility management in electricity markets Amin Masoumzadeh, Ehsan Nekouei and Tansu Alpcan.
