EDITORIAL article
Front. Energy Res.
Sec. Smart Grids
Volume 13 - 2025 | doi: 10.3389/fenrg.2025.1633454
This article is part of the Research TopicLearning-assisted Diagnosis and Control of Electric Distribution NetworkView all 13 articles
Editorial: Learning-assisted Diagnosis and Control of Electric Distribution Network
Provisionally accepted
- 1Jinling Institute of Technology, Nanjing, China
- 2Wuhan University of Technology, Wuhan, China
- 3Wuhan University, Wuhan, China
- 4National Renewable Energy Laboratory, Golden, United States
- 5University of Denver, Denver, United States
Select one of your emails
You have multiple emails registered with Frontiers:
Notify me on publication
Please enter your email address:
If you already have an account, please login
You don't have a Frontiers account ? You can register here
The digital transformation of modern power systems establishes a robust digital foundation for enhanced system observability, operational transparency, and intelligent planning. This paradigm shift centers around harnessing big data resources spanning the entire energy value chain, from generation to end-user consumption (Channamallu et al., 2025). While low-voltage distribution networks face unprecedented challenges from the proliferation of renewable energy integration and electric vehicle penetration, emerging opportunities arise from ubiquitous sensing infrastructure and advanced control architectures (He et al., 2022). Artificial intelligence (AI) has emerged as a pivotal enabler to unlock the latent value of these multidimensional datasets, offering transformative solutions across critical domains including real-time fault diagnostics, adaptive control systems, and holistic grid optimization (Zhang, et al., 2023). The imperative for AI adoption becomes particularly pronounced in modern distribution networks, where escalating topological complexity and dynamic operating conditions necessitate proactive management frameworks and self-healing capabilities (Alam, et al., 2024).Conventional model-driven approaches, reliant on physical mechanism interpretation and static control paradigms, demonstrate inherent limitations in adapting to frequent network reconfigurations characteristic of distribution-level operations (Chen, et al., 2024). Nevertheless, direct implementation of existing AI/ML algorithms remains constrained by stringent power system requirements.
Keywords: state estimation, energy storage, Electric distribution network, Diagnostics and prognostics, robust control
Received: 22 May 2025; Accepted: 02 Jun 2025.
Copyright: © 2025 Zhang, tang, Wang, Yan and Gao. 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:
Chaolong Zhang, Jinling Institute of Technology, Nanjing, China
Xiao Wang, Wuhan University, Wuhan, China
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.