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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.
- 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
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
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
- No. of pages:
- © Elsevier Science 2017
- 27th November 2017
- Elsevier Science
- eBook ISBN:
- Paperback ISBN:
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.
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.
Assistant Prof. in Electrical Engineering, Florida State University
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.
Ph.D candidate, Department of EECS, UC Berkeley
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