Big Data Application in Power Systems - 1st Edition - ISBN: 9780128119686

Big Data Application in Power Systems

1st Edition

Editors: Reza Arghandeh Yuxun Zhou
Paperback ISBN: 9780128119686
Imprint: Elsevier Science
Published Date: 8th December 2017
Page Count: 500
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Description

Big Data Application in Power Systems brings together experts from academia, industry and regulatory agencies who share their understanding and discuss the big data analytics applications for power systems diagnostics, operation and control. Recent developments in monitoring systems and sensor networks dramatically increase the variety, volume and velocity of measurement data in electricity transmission and distribution level. The book focuses on rapidly modernizing monitoring systems, measurement data availability, big data handling and machine learning approaches to process high dimensional, heterogeneous and spatiotemporal data. The book chapters discuss challenges, opportunities, success stories and pathways for utilizing big data value in smart grids.

Key Features

  • Provides expert analysis of the latest developments by global authorities
  • Contains detailed references for further reading and extended research
  • Provides additional cross-disciplinary lessons learned from broad disciplines such as statistics, computer science and bioinformatics
  • Focuses on rapidly modernizing monitoring systems, measurement data availability, big data handling and machine learning approaches to process high dimensional, heterogeneous and spatiotemporal data

Readership

Engineers, scientists, future electric grid professionals, researchers, graduate students, lecturers in electricity network and smart grid, data analysis experts, developers in electricity networks and advanced technologies for smart grids

Table of Contents

Contributors
About the editors
Preface: objective and overview of the book
Acknowledgments

Section 1: Harness the Big Data From Power Systems
1. A Holistic Approach to Becoming a Data-Driven Utility
2. Emerging Security and Data Privacy Challenges for Utilities: Case Studies and Solutions
3. The Role of Big Data and Analytics in Utility Innovation
4. Frameworks for Big Data Integration, Warehousing, and Analytics

Section 2: Harness the Power of Big data
5. Moving Toward Agile Machine Learning for Data Analytics in Power Systems
6. Unsupervised Learning Methods for Power System Data Analysis
7. Deep Learning for Power System Data Analysis
8. Compressive Sensing for Power System Data Analysis
9. Time-Series Classification Methods: Review and Applications to Power Systems Data

Section 3: Put the Power of Big Data into Power Systems
10. An Overview of Big Data Application in Power Transmission and Distribution Networks
11. On Data-Driven Approaches for Demand Response
12. Topology Learning in Radial Distribution Grids
13. Grid Topology Identification Via Distributed Statistical Hypothesis Testing
14. Supervised Learning-Based Fault Location in Power Grids
15. Data-Driven Voltage Unbalance Analysis in Power Distribution Networks
16. Predictive Analytics for Comprehensive Energy Systems State Estimation
17. Data Analytics for Energy Disaggregation: Methods and Applications
18. Energy Disaggregation and the Utility-Privacy Tradeoff

INDEX

Details

No. of pages:
500
Language:
English
Copyright:
© Elsevier Science 2018
Published:
Imprint:
Elsevier Science
Paperback ISBN:
9780128119686

About the Editor

Reza  Arghandeh

Reza Arghandeh

Reza Arghandeh - Assistant Prof. in Electrical Engineering, Florida State University

Contributed to the Elsevier publication “Renewable Energy Integration: Practical Management of Variability, Uncertainty and Flexibility” and has published more than 20 journal papers related to smart grid technologies, monitoring systems, data analysis for control, and diagnostic application in power systems.

Research Interests

Distributed Control, Data Analysis, Modeling & Simulation Tools for Power System/Power Electronics Diagnostics and Operation applications including but not limited to: Distributed Energy Resources Integration; Buildings, Mircogrids and Distribution Management Systems (BMS-DMS); Grid Automation; Adaptive Demand Response; Electric Grid Observability; Distribution Network Model Validation; Smart Grid Cyber-Physical Resilience; and Internet of Power Electronic Inverters.

Affiliations and Expertise

Assistant Prof. in Electrical Engineering, Florida State University

Yuxun Zhou

Yuxun Zhou is currently a Ph.D candidate at Department of EECS, UC Berkeley. Prior to that, he obtained the Diplome d’Ingenieur in Applied Mathematics from Ecole Centrale Paris, and a B.S. degree from Xi’an Jiaotong University. Yuxun has published more than 20 refereed articles, and has received several student awards. His research interest is on machine learning and control methods for modern sensor rich, ubiquitously connected energy systems, including smart buildings, power distribution networks, power systems with renewable integration, etc. Among others, he has designed novel algorithms for non-convex machine learning problems, and has constructed a series of event detection methods for complex systems with resource and information constraints.

Affiliations and Expertise

Ph.D candidate, Department of EECS, UC Berkeley