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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.

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.

Wide area monitoring, protection and control systems (WAMPACs) have been recognized as the most promising enabling technologies to meet challenges of modern electric power transmission systems, where reliability, economics, environmental and other social objectives must be balanced to optimize the grid assets and satisfy growing electrical demand. To this aim WAMPAC requires precise phasor and frequency information, which are acquired by deploying multiple time synchronized sensors, known as Phasor Measurement Units (PMUs), providing precise synchronized information about voltage and current phasors, frequency and rate-of-change-of-frequency.