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

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

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