- 250 pages
- English
- ePUB (mobile friendly)
- Available on iOS & Android
eBook - ePub
Data Fusion and Data Mining for Power System Monitoring
About this book
Data Fusion and Data Mining for Power System Monitoring provides a comprehensive treatment of advanced data fusion and data mining techniques for power system monitoring with focus on use of synchronized phasor networks. Relevant statistical data mining techniques are given, and efficient methods to cluster and visualize data collected from multiple sensors are discussed. Both linear and nonlinear data-driven mining and fusion techniques are reviewed, with emphasis on the analysis and visualization of massive distributed data sets. Challenges involved in realistic monitoring, visualization, and analysis of observation data from actual events are also emphasized, supported by examples of relevant applications.
Features
- Focuses on systematic illustration of data mining and fusion in power systems Covers issues of standards used in the power industry for data mining and data analytics
- Applications to a wide range of power networks are provided including distribution and transmission networks
- Provides holistic approach to the problem of data mining and data fusion using cutting-edge methodologies and technologies
- Includes applications to massive spatiotemporal data from simulations and actual events
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Please note we cannot support devices running on iOS 13 and Android 7 or earlier. Learn more about using the app.
Yes, you can access Data Fusion and Data Mining for Power System Monitoring by Arturo Román Messina in PDF and/or ePUB format, as well as other popular books in Mathematics & Applied Mathematics. We have over one million books available in our catalogue for you to explore.
Information
Table of contents
- Cover
- Half Title
- Title Page
- Copyright Page
- Table of Contents
- Preface
- Author
- List of Abbreviations
- Section I Overview of Modern Wide-Area Monitoring Systems
- Section II Advanced Projection-Based Data Mining and Data Fusion Techniques
- Section III Challenges and Opportunities in the Application of Data Mining and Data Fusion Techniques
- Index