Data Fusion and Data Mining for Power System Monitoring
CRC Press (Verlag)
978-0-367-33367-6 (ISBN)
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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
Arturo Messina earned his PhD from Imperial College, London, UK, in 1991. Since 1997, he has been a professor in the Center for Research and Advanced Studies, Guadalajara, Mexico. He is on the editorial and advisory boards of Electric Power Systems Research, and Electric Power Components and Systems. From 2011 to 2018 he was Editor of the IEEE Trans. on Power Systems and Chair of the Power System Stability Control Subcommittee of the Power Systems Dynamic Committee of IEEE (2015-2018). A Fellow of the IEEE, he is the editor of Inter-Area Oscillations in Power Systems – A Nonlinear and Non-stationary Perspective (Springer, 2009) and the author of Robust Stability and Performance Analysis of Large-Scale Power Systems with Parametric Uncertainty: A Structured Singular Value Approach (Nova Science Publishers, 2009), and Wide-Area Monitoring of Interconnected Power Systems (IET, 2015).
1. Introduction. 2. Data mining and data fusion architectures. 3. Data parameterization, clustering and denoising. 4. Spatio-temporal data mining. 5. Multisensor data fusion. 6. Dimensionality reduction and feature extraction and classification. 7. Forecasting decision support systems. 8. Data fusion and data mining analysis and visualization. 9. Emerging topics in data mining and data fusion. 10. Experience with the application of data fusion and data mining for power system health monitoring.
| Erscheinungsdatum | 22.05.2020 |
|---|---|
| Zusatzinfo | 32 Tables, black and white; 128 Illustrations, black and white |
| Verlagsort | London |
| Sprache | englisch |
| Maße | 156 x 234 mm |
| Gewicht | 580 g |
| Themenwelt | Mathematik / Informatik ► Mathematik |
| Technik ► Elektrotechnik / Energietechnik | |
| Technik ► Umwelttechnik / Biotechnologie | |
| ISBN-10 | 0-367-33367-8 / 0367333678 |
| ISBN-13 | 978-0-367-33367-6 / 9780367333676 |
| Zustand | Neuware |
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