Big Data Application in Power Systems 1st Edition

Big Data Application in Power Systems 1st Edition – Ebook Instant Download/Delivery ISBN(s): 9780128119686,0128119683,9780128119693, 0128119691

Product details:

• ISBN-10: 0128119691
• ISBN-13: 9780128119693
• Author: Reza Arghandeh, Yuxun Zhou

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.

Table contents:

Chapter 1: A Holistic Approach to Becoming a Data-Driven Utility

Chapter 2: Emerging Security and Data Privacy Challenges for Utilities: Case Studies and Solutions

Chapter 3: The Role of Big Data and Analytics in Utility Innovation

Chapter 4: Frameworks for Big Data Integration, Warehousing, and Analytics

Chapter 5: Moving Toward Agile Machine Learning for Data Analytics in Power Systems

Chapter 6: Unsupervised Learning Methods for Power System Data Analysis

Chapter 7: Deep Learning for Power System Data Analysis

Chapter 8: Compressive Sensing for Power System Data Analysis

Chapter 9: Time-Series Classification Methods: Review and Applications to Power Systems Data

Chapter 10: Future Trends for Big Data Application in Power Systems

Chapter 11: On Data-Driven Approaches for Demand Response

Chapter 12: Topology Learning in Radial Distribution Grids

Chapter 13: Grid Topology Identification via Distributed Statistical Hypothesis Testing

Chapter 14: Supervised Learning-Based Fault Location in Power Grids

Chapter 15: Data-Driven Voltage Unbalance Analysis in Power Distribution Networks

Chapter 16: Predictive Analytics for Comprehensive Energy Systems State Estimation

Chapter 17: Data Analytics for Energy Disaggregation: Methods and Applications

Chapter 18: Energy Disaggregation and the Utility-Privacy Tradeoff

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