Real-time monitoring of the power electric grid is more important than ever, to preventcatastrophic failures and to support fast-acting power electronic devices, renewable energy resources, and extreme weather conditions. Accordingly, there is an emerging need to a new class of wide-area monitoring sensors that can capture time-synchronized voltage and current waveforms. This thesis is about the new frontier in the power system monitoring using synchronized waveform measurements.

Waveform Measurement Units (WMUs) are a new class of smart grid sensors thatprovide precise time-synchronized voltage waveform and current waveform measurements, also known as synchro-waveform measurements. WMUs can show the wave-shape of the voltage and current at very high resolutions. Further, the waveform measurements are precisely synchronized across different WMUs. The very high reporting rate of WMUs and the fact that we have access to synchronized waveform measurements, can significantly enhance our understanding and awareness about the status of the power electric grid and its components. However, the sole availability of such huge amount of data in itself is not sufficient; we need to translate the WMUs data to actionable information to be useful. This thesis provides new methodologies for the practical applications of synchro- waveform measurements in event detection, event classification, event location identification, and event-based network parameters estimation. An event is defined as any sort of change in any component across the power electric grid, with focus on sub-cycle events; which are the type of events that call for the use of waveform measurements. This thesis also presents real-life applications of synchronized waveform data for asset monitoring and wildfire monitoring.