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Big Data Application in Power Systems

  • ID: 4081000
  • Book
  • November 2017
  • 480 Pages
  • Elsevier Science and Technology
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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

Please Note: This is an On Demand product, delivery may take up to 11 working days after payment has been received.

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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. Future Trends for Big Data Application in Power Systems 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

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Arghandeh, Reza
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.
Zhou, Yuxun
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.

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