Big Data Application in Power Systems

Big Data Application in Power Systems

1st Edition - November 27, 2017

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  • Editors: Reza Arghandeh, Yuxun Zhou
  • eBook ISBN: 9780128119693
  • Paperback ISBN: 9780128119686

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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

  • SECTION 1 Harness the Big Data From Power Systems
    1. A Holistic Approach to Becoming a Data-Driven Utility
    John D. McDonald, GE Energy Connections-Grid Solutions, Atlanta, GA, United States
    2. Emerging Security and Data Privacy Challenges for Utilities: Case Studies and Solutions
    Carol L. Stimmel, Manifest Mind, LLC, Canaan, NY, United States
    3. The Role of Big Data and Analytics in Utility Innovation
    Jeffrey S. Katz, IBM, Hartford, CT, United States
    4. Frameworks for Big Data Integration, Warehousing, and Analytics 
    Feng Gao, Tsinghua University Energy Internet Research Institute, Beijing, China

    SECTION 2 Harness the Power of Big data
    5. Moving Toward Agile Machine Learning for Data Analytics in Power Systems
    Yuxun Zhou, and Reza Arghandeh, UC Berkeley and Florida State University, Tallahassee, FL, United States
    6. Unsupervised Learning Methods for Power System Data Analysis
    Thierry Zufferey*, Andreas Ulbig*†, Stephan Koch*†, Gabriela Hug*
     ETH Zurich, Power Systems Laboratory, Zurich, Switzerland, Adaptricity AG, c/o ETH Zurich, Power Systems Laboratory, Zurich, Switzerland

    7. Deep Learning for Power System Data Analysis
     Elena Mocanu, Phuong H. Nguyen, Madeleine Gibescu, Eindhoven University of Technology, Eindhoven, The Netherlands
    8. Compressive Sensing for Power System Data Analysis 
    Mohammad Babakmehr*, Mehrdad Majidi†, Marcelo G. Simoes*
    Colorado School of Mines, Golden, CO, United States
    University of Nevada, Reno, NV, United States

    9. Time-Series Classification Methods: Review and Applications to Power Systems Data
    Gian Antonio Susto, Angelo Cenedese, Matteo Terzi, University of Padova, Padova, Italy

    SECTION 3 Put the Power of Big Data into Power Systems
    10. Future Trends for Big Data Application in Power Systems
    Ricardo J. Bessa, INESC Technology and Science—INESC TEC, Porto, Portugal
    11. On Data-Driven Approaches for Demand Response
    Akin Tascikaraoglu, Mugla Sitki Kocman University, Mugla, Turkey
    12. Topology Learning in Radial Distribution Grids
    Deepjyoti Deka, Michael Chertkov, Los Alamos National Laboratory, Los Alamos, NM, United States
    13. Grid Topology Identification via Distributed Statistical Hypothesis Testing
    Saverio Bolognani, Automatic Control Laboratory ETH Zurich, Zurich, Switzerland
    14. Supervised Learning-Based Fault Location in Power Grids
    Hanif Livani, University of Nevada Reno, Reno, NV, United States
    15. Data-Driven Voltage Unbalance Analysis in Power Distribution Networks
    Matthias Stifter*, Ingo Nader AIT Austrian Institute of Technology, Center of Energy, Vienna, Austria
    Unbelievable Machine, Vienna, Austria

    16. Predictive Analytics for Comprehensive Energy Systems State Estimation
    Yingchen Zhang*, Rui Yang*, Jie Zhang†, Yang Weng‡, Bri-Mathias Hodge*
    National Renewable Energy Laboratory, Golden, CO, United States
    University of Texas at Dallas, Richardson, TX, United States
    Arizona State University, Tempe, AZ, United States

    17. Data Analytics for Energy Disaggregation: Methods and Applications
    Behzad Najafi, Sadaf Moaveninejad, Fabio Rinaldi, Polytechnic University of Milan, Milan, Italy
    18. Energy Disaggregation and the Utility-Privacy Tradeoff
    Roy Dong*, Lillian J. Ratliff†
    University of California, Berkeley, Berkeley, CA, United States
    University of Washington, Seattle, WA, United States

Product details

  • No. of pages: 480
  • Language: English
  • Copyright: © Elsevier Science 2017
  • Published: November 27, 2017
  • Imprint: Elsevier Science
  • eBook ISBN: 9780128119693
  • Paperback ISBN: 9780128119686

About the Editors

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

Ratings and Reviews

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  • Thomas Sun Sep 01 2019

    Timely Book

    covers ML applications in smart grid, which is great.