Big Data Application in Power Systems

Name: Big Data Application in Power Systems
Price: 165 USD
Availability: InStock
ISBN: 9780443215247

by Reza Arghandeh, Yuxun Zhou

2nd Edition

Publisher: Elsevier Science

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Electrical Engineering

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Book Details

Print ISBN	9780443215247
eText ISBN	9780443219511
Publisher	Elsevier Science
Publishing Year	2024
Edition	2nd Edition
Language	English
Pages	448

Big Data Application in Power Systems, 2nd Edition provides systematic computational methods for applying big data analytics to power system diagnostics, grid operation, and network control. Editors Reza Arghandeh and Yuxun Zhou gather practical machine learning frameworks specifically created for processing high-dimensional, heterogeneous, and spatiotemporal grid measurements. The text details step-by-step guidance to help technical teams build data-driven utility operations.

Structurally, the publication divides its coverage into three main sections covering utility broad perspectives, theoretical formulations, and application-driven case studies. To address security risks and data privacy challenges within data-driven electric utilities, the thematic focus incorporates cross-disciplinary methodologies adapted from statistics, computer science, and bioinformatics.

A key feature of this second edition is the inclusion of five new chapters that address emerging technological solutions and current industry topics. This comprehensive volume supports academic researchers, graduate students, university professors, power network scientists, field engineers, software developers, and data analysis experts across smart grid environments.

Chapter 1: A Holistic Approach to Becoming a Data-driven Utility
Chapter 2: Security and Data Privacy Challenges for Data-driven Utilities
Chapter 3: The Role of Big Data and Analytics in Utilities Innovation
Chapter 4: Big Data integration for the digitalisation and decarbonisation of distribution grids
Chapter 5: Topology Detection in Distribution Networks with Machine Learning
Chapter 6: Grid Topology Identification via Distributed Statistical Hypothesis Testing
Chapter 7: Learning Stable Volt/Var Controllers in Distribution Grids
Chapter 8: Grid-edge Optimization and Control with Machine Learning
Chapter 9: Fault Detection in Distribution Grid with Spatial-Temporal Recurrent Graph Neural Networks
Chapter 10: Distribution Networks Events Analytics using Physics-Informed Graph Neural Networks
Chapter 11: Transient Stability Predictions in Power Systems using Transfer Learning
Chapter 12: Misconfiguration Detection of Inverter-based Units in Power Distribution Grids using Machine Learning
Chapter 13: Virtual Inertia Provision from Distribution Power Systems using Machine Learning
Chapter 14: Electricity Demand Flexibility Estimation in Warehouses using Machine Learning
Chapter 15: Big Data Applications in Electric Power Systems: The Role of Explainable Artificial Intelligence (XAI) in Smart Grids
Chapter 16: Photovoltaic and Wind Power Forecasting Using Data-Driven Techniques: an overview and a distribution-level case study
Chapter 17: Grid resilience against wildfire with Machine Learning