Power systems are becoming increasingly complex, handling rising shares of distributed intermittent renewable generation, EV charging stations, and storage. To ensure power availability and quality, the grid needs to be monitored as a whole, by wide area monitoring (WAM), not just in small sections separately. Parameter oscillations need to be detected and acted upon. This requires sensors, data assimilation and visualization, comparison with models, modelling, and system architectures for different grid types. This hands-on reference for researchers in power systems, professionals at grid operators and grid equipment manufacturers, as well as for advanced students, offers a comprehensive treatment of advanced data-driven signal processing techniques for the analysis and characterization of system data and transient oscillations in power grids. Algorithms and examples help readers understand the material. Challenges involved in realistic monitoring, visualization, and analysis of actual disturbance events are emphasized. Chapters in this second edition cover WAM and analysis systems, WAM system architectures, modelling of power system dynamic processes, data processing and feature extraction, multi-sensor multitemporal data fusion, WAM of power systems with high penetration of distributed generation, distributed wide-area oscillation monitoring, near real-time analysis and monitoring, and interpretation and visualization of wide-area PMU measurements.

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

The study of complex dynamic processes governed by nonlinear and nonstationary characteristics is a problem of great importance in the analysis and control of power system oscillatory behavior. Power system dynamic processes are highly random, nonlinear to some extent, and intrinsically nonstationary even over short time intervals as in the case of severe transient oscillations in which switching events and control actions interact in a complex manner. Phenomena observed in power system oscillatory dynamics are diverse and complex. Measured ambient data are known to exhibit noisy, nonstationary fluctuations resulting primarily from small magnitude, random changes in load, driven by low-scale motions or nonlinear trends originating from slow control actions or changes in operating conditions. Forced oscillations resulting from major cascading events, on the other hand, may contain motions with a broad range of scales and can be highly nonlinear and time-varying. Prediction of temporal dynamics, with the ultimate application to real-time system monitoring, protection and control, remains a major research challenge due to the complexity of the driving dynamic and control processes operating on various temporal scales that can become dynamically involved. An understanding of system dynamics is critical for reliable inference of the underlying mechanisms in the observed oscillations and is needed for the development of effective wide-area measurement and control systems, and for improved operational reliability.

This book provides a compact yet comprehensive treatment of advanced data-driven signal processing techniques for the analysis and characterization of both ambient power system data and transient oscillations resulting from major disturbances. Inspired by recent developments in multi-sensor data fusion, multi-temporal data assimilation techniques for power system monitoring are proposed and tested in the context of modern wide-area monitoring system architectures. Recent advances in understanding and modeling nonlinear, time-varying power system processes are reviewed and factors affecting the performance these techniques are discussed. A number of algorithms and examples are presented throughout the text as an aid to understanding the basic material provided. Challenges involved in realistic monitoring, visualization and analysis of actual disturbance events are emphasized and examples of applications to a wide range of power networks are provided. Topics covered include: wide-area monitoring and analysis systems; wide-area monitoring system architectures; spatio-temporal modeling of power system dynamic processes; advanced data processing and feature extraction; multi-sensor multitemporal data fusion; monitoring the status of the system; near real-time analysis and monitoring; and interpretation and visualization of wide-area PMU measurements. This book will be of interest to students, researchers and engineers involved in the study, design, analysis and control of power systems.