REVIEW article
Front. Electron.
Sec. Power Electronics
PMU Advancements and Applications in the AI Era
Provisionally accepted- 1Shandong Development New Energy Co., Ltd, Jinan, China
- 2Shandong University, Jinan, China
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Phasor Measurement Units (PMUs) have long been central to wide-area power system monitoring. However, the growing penetration of inverter-based resources (IBRs) and emerging data-driven analytics highlight limitations of conventional phasor abstractions in capturing fast electromagnetic dynamics. This paper provides a critical review of recent PMU advancements, emphasizing the transition from phasor-based measurements toward high-fidelity point-on-wave (PoW) sensing. Unlike existing surveys, this work evaluates the state of the art across three dimensions: (i) hardware evolution toward edge-capable sensing, (ii) AI-enabled estimation and analytics for waveform-scale data, and (iii) deployment strategies in complex environments such as microgrids and data-center-rich grids. Key bottlenecks in data acquisition and processing are summarized, and representative commercial PMU solutions are compared with emerging PoW-oriented technologies from both capability and cost-implication perspectives. An illustrative field case is included to show how waveform-level processing can reveal oscillatory components that may be attenuated under conventional multi-cycle processing. The review concludes with a strategic roadmap to bridge academic AI models with practical grid applications.
Keywords: AI in power systems, Damping control, data centers, PMU, point-on-wave, wide-area monitoring
Received: 06 Sep 2025; Accepted: 04 Feb 2026.
Copyright: © 2026 Chen, Xu, Li, Tian, Yan, Han and Lou. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Jie Lou
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
