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 November 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

1. Harness the Big data from Power Systems

Chapter 1.1. A Holistic Approach to Becoming a Data-driven Utility 
     

Chapter 1.2. Security and Data Privacy Challenges for Utilities 

Chapter 1.3. The Rule of Big Data and Analytics in Utilities Innovation 
     

Chapter 1.4. Frameworks for Big data Integration, Warehousing, and Analytics 
      

Chapter 1.5. Future Trends For Big data Application In Power System 

2. Harness the Power of Big data

Chapter 2.1. Feature Selection Methods for Power System Data 
     

Chapter 2.2. Supervised and unsupersived Methods for Data Analytics in Power Systems 
     

Chapter 2.3. Deep Learning for Power System Data Analysis 
     

Chapter 2.4. Compressive Sensing for Power System Data Analysis 
     

Chapter 2.5. Time Series Classification Methods for Power Systems Data 

3. Put the Power of Big data into Power Systems
     

Chapter 3.1. Unsupervised Learning Methods for Power System Data Analysis
     

Chapter 3.2. On Data-Driven Approaches for Demand Response 
     

Chapter 3.3. Learning-based Approaches for Distribution Network Topology Detection 
     

Chapter 3.4. Learning to Make Optimal Decisions Under Uncertainty in Smart Grids 
     

Chapter 3.5. Data Analytics for Energy Disaggregation 
     

Chapter 3.6. Grid Topology Identification via Distributed Statistical Hypothesis Testing 
     

Chapter 3.7. Merging the Benefit of Social Network and Power System Data 

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 - 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