WAVEFORMS ARE THE MOST GRANULAR ANDauthentic representation of voltage and current in power systems. With the latest advancements in power system measurement technologies, it is now possible to obtain time-synchronized waveform measurements, i.e., synchrowaveforms, from different locations of a power system. The measurement technology to obtain synchro-waveforms is referred to as a waveform measurement unit (WMU). WMUs can capture the most inconspicuous disturbances that are overlooked by other types of time-synchronized sensors, such as phasor measurement units (PMUs). WMUs also monitor system dynamics at much higher frequencies as well as much lower frequencies than the fundamental components of voltage and current that are commonly monitored by PMUs. Thus, synchro-waveforms introduce a ew frontier to advance power system and equipment monitoring and control, with direct applications in situational awareness, system dynamics tracking, incipient fault detection and identification, condition monitoring, and so on. They also play a critical role in monitoring inverter-based resources (IBR) due to the high-frequency switching characteristics of IBRs. Accordingly, in this article, we provide a high-level overview of this new and emerging technology and its implications, discussing the latest advancements in the new field of synchro waveforms, including basic principles, real-world examples, potentials in data analytics, and innovative applications
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Data-Driven Models for Sub-Cycle Dynamic Response of Inverter-Based Resources Using WMU Measurements
Using real-world data from Waveform Measurement Units (WMUs), this letter proposes novel data-driven methods to model the dynamic response of inverter-based resource (IBR) to the high-frequency disturbances that occur in practice in power systems. WMUs are an emerging class of smart grid sensors. They can capture the fast sub-cycle dynamics in power systems, which are overlooked by phasor measurement units (PMUs). After extracting the differential voltage and current waveforms from the raw waveform data, we develop multiple methods that include data-driven model library construction and proper model selection. One class of methods is proposed in frequency domain, which is based on modal analysis. Another class of methods is proposed in time domain, which is based on regression analysis of time-series. Experimental results based on real-world WMU data demonstrate the of performance the proposed methods.
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- Award ID(s):
- 2152258
- PAR ID:
- 10510997
- Publisher / Repository:
- IEEE
- Date Published:
- Journal Name:
- IEEE Transactions on Smart Grid
- Volume:
- 14
- Issue:
- 5
- ISSN:
- 1949-3053
- Page Range / eLocation ID:
- 4125 to 4128
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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