Big Data Application In Power Systems by Reza ArghandehBig Data Application In Power Systems by Reza Arghandeh

Big Data Application In Power Systems

byReza ArghandehEditorYuxun Zhou

Paperback | November 27, 2017

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Big Data Application in Power Systemsbrings 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.



  • 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
Reza Arghandeh - Assistant Prof. in Electrical Engineering, Florida State UniversityContributed 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 fo...
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Title:Big Data Application In Power SystemsFormat:PaperbackDimensions:480 pages, 8.75 × 6.35 × 0.68 inPublished:November 27, 2017Publisher:Elsevier ScienceLanguage:English

The following ISBNs are associated with this title:

ISBN - 10:0128119683

ISBN - 13:9780128119686

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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 UtilityJohn D. McDonald, GE Energy Connections-Grid Solutions, Atlanta, GA, United States2. Emerging Security and Data Privacy Challenges for Utilities: Case Studies and SolutionsCarol L. Stimmel, Manifest Mind, LLC, Canaan, NY, United States3. The Role of Big Data and Analytics in Utility InnovationJeffrey S. Katz, IBM, Hartford, CT, United States4. Frameworks for Big Data Integration, Warehousing, and AnalyticsFeng Gao, Tsinghua University Energy Internet Research Institute, Beijing, ChinaSECTION 2 Harness the Power of Big data 5. Moving Toward Agile Machine Learning for Data Analytics in Power SystemsYuxun Zhou, and Reza Arghandeh, UC Berkeley and Florida State University, Tallahassee, FL, United States6. Unsupervised Learning Methods for Power System Data AnalysisThierry Zufferey*, Andreas Ulbig* , Stephan Koch* , Gabriela Hug* * ETH Zurich, Power Systems Laboratory, Zurich, Switzerland Adaptricity AG, c/o ETH Zurich, Power Systems Laboratory, Zurich, Switzerland7. Deep Learning for Power System Data AnalysisElena Mocanu, Phuong H. Nguyen, Madeleine Gibescu, Eindhoven University of Technology, Eindhoven, The Netherlands8. Compressive Sensing for Power System Data AnalysisMohammad Babakmehr*, Mehrdad Majidi , Marcelo G. Simoes* * Colorado School of Mines, Golden, CO, United States University of Nevada, Reno, NV, United States9. Time-Series Classification Methods: Review and Applications to Power Systems DataGian Antonio Susto, Angelo Cenedese, Matteo Terzi, University of Padova, Padova, ItalySECTION 3 Put the Power of Big Data into Power Systems 10. Future Trends for Big Data Application in Power SystemsRicardo J. Bessa, INESC Technology and Science-INESC TEC, Porto, Portugal11. On Data-Driven Approaches for Demand ResponseAkin Tascikaraoglu, Mugla Sitki Kocman University, Mugla, Turkey12. Topology Learning in Radial Distribution GridsDeepjyoti Deka, Michael Chertkov, Los Alamos National Laboratory, Los Alamos, NM, United States13. Grid Topology Identification via Distributed Statistical Hypothesis TestingSaverio Bolognani, Automatic Control Laboratory ETH Zurich, Zurich, Switzerland14. Supervised Learning-Based Fault Location in Power GridsHanif Livani, University of Nevada Reno, Reno, NV, United States15. Data-Driven Voltage Unbalance Analysis in Power Distribution NetworksMatthias Stifter*, Ingo Nader * AIT Austrian Institute of Technology, Center of Energy, Vienna, Austria Unbelievable Machine, Vienna, Austria16. Predictive Analytics for Comprehensive Energy Systems State EstimationYingchen 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 States17. Data Analytics for Energy Disaggregation: Methods and ApplicationsBehzad Najafi, Sadaf Moaveninejad, Fabio Rinaldi, Polytechnic University of Milan, Milan, Italy18. Energy Disaggregation and the Utility-Privacy TradeoffRoy Dong*, Lillian J. Ratliff * University of California, Berkeley, Berkeley, CA, United States University of Washington, Seattle, WA, United States

Index