Data fusion and data mining for power system monitoring. (2020)

• Record Type: Book
• Further Information: Note: Arturo Román Messina.
• Authors: Messina, Arturo R
• Contents: Chapter 1 Introduction; 1.1.- Introduction to power system monitoring; 1.2.- Wide-area power system monitoring; 1.3.- Data fusion and data mining for power health monitoring; 1.4.- Dimensionality reduction; 1.5.- Distribution system monitoring; 1.6.- Power system data; 1.7.- Sensor placement for system monitoring; 1.8.- References; Chapter 2 Data mining and data fusion architectures; 2.1.- Introduction; 2.2.- Trends in data fusion and data monitoring; 2.3.- Data mining and data fusion for enhanced monitoring; 2.4.- Data fusion architectures for power system monitoring; 2.5.- Open issues in data fusion; 2.6.- References; Chapter 3 Data parameterization, clustering and denoising; 3.1.- Introduction: Backgroung and driving forces; 3.2.- Spatio-temporal data sets projections and spatial maps; 3.3.- Power system data normalization and scaling; 3.4.- Nonlinear dimensionality reduction; 3.5.- Clustering schemes; 3.6.- Detrending and denoising of power system oscillations; 3.7.- References Chapter 4 Spatio-temporal data mining; 4.1.- Introduction; 4.2.- Data mining and knowledge discovery; 4.3.- Spatio-temporal modeling of dynamic processes ; 4.4.- Space-time prediction and forecasting; 4.5.- Space-temporal data mining and pattern evaluation; 4.6.- References; Chapter 5 Multisensor data fusion; 5.1.- Introduction and motivation; 5.2.- Spatio-temporal data fusion; 5.3.- Data fusion principles; 5.4.- Multisensor data fusion framework; 5.5.- Multimodal data fusion techniques; 5.6.- Case study; 5.7.- References; Chapter 6 Dimensionality reduction and feature extraction and classification; 6.1.- Background and driving forces; 6.2.- Fundamentals of dimensionality reduction; 6.3.- Data-driven feature extraction procedures; 6.4.- Dimensionality reduction methods; 6.5.- Dimensionality reduction for classification and cluster validation; 6.6.- Markov dynamic spatio temporal models; 6.7.- Sensor selection and placement; 6.8.- Open problems in nonlinear dimensionality reduction; 6.9.- References; Chapter 7 Forecasting decision support systems; 7.1.- Introduction; 7.2.- Backgroud: Early warning and decision support systems; 7.3.- Data-driven prognostics; 7.4.- Space-time forecasting and prediction; 7.5.- Kalman flitering approach to system forecasting; 7.6.- Dynamic harmonic regression; 7.7.- Damage detection; 7.8.- Power systems time series forecasting; 7.9.- Anomaly detection in time series; 7.10.- References; Chapter 8 Data fusion and data mining analysis and visualization; 8.1.- Introduction; 8.2.- Advanced visualization techniques; 8.3.- Multivariable modeling and visualization; 8.4.- Cluster-based visualization of multidimensional data; 8.5.- Spatial and network displays; 8.6.- References; Chapter 9 Emerging topics in data mining and data fusion; 9.1.- Introduction; 9.2.- Dynamic spatio-temporal modelling 9.3.- Challenges for the analysis of high-dimensional data; 9.4.- Distributed data mining; 9.5.- Dimensionality reduction; 9.6.- Bio-inspired data mining and data fusion; 9.7.- Other emerging issues; 9.8.- Application to power system data; 9.9.- References; Chapter 10 Experience with the application of data fusion and data mining for power system health monitoring; 10.1.- Introduction; 10.2.- Background; 10.3.- Sensor placement; 10.4.- Cluster-based visualization of transient performance; 10.5.- Multimodal fusion of observational data; 10.6.- References;
• Edition: 1st
• Publisher Details: Boca Raton : CRC Press
• Publication Date: 2020
• Extent: 1 online resource, illustrations (black and white)
• Subjects: 621.31; Electric power systems; Data mining
• Languages: English
• ISBNs: 9781000065930; 9781000065893; 9780429319440
• Related ISBNs: 9780367333676
• Notes: Note: Description based on CIP data; resource not viewed.
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