Rising electricity demand and higher consumption levels are pushing power systems toward stricter safety and stability requirements. To maintain dependable, secure, and efficient transmission and distribution, it is essential to continuously monitor system conditions and swiftly identify faults. This relies on aggregating and interpreting data from numerous sensors and measurement units placed across grid assets. Real-time health assessment of equipment enables early warning of potential failures, preventing them from developing into major incidents while also boosting overall grid performance. Over the past few decades, breakthroughs in data analytics, machine learning, and artificial intelligence have dramatically strengthened monitoring and diagnostic functions. Combining data from diverse sources improves anomaly detection accuracy, thereby reducing service disruptions, strengthening network reliability, and cutting maintenance costs. Furthermore, the broad adoption of these advanced technologies has also helped mitigate the environmental impact of power systems. This Research Topic, serves as a continuation of the previous Research Topic with 16 published articles, entitled “New Advancement in Power System Condition Monitor, Fault Diagnosis and Environmental Compatibility”, collected 8 articles.
The first research area of this Research Topic is about Power System Condition Monitor and Optimization, which collected 5 papers. Liu et al. reported YOLOv8-FOD, a lightweight foreign object detection algorithm tailored for deployment on resource-constrained edge devices in power transmission line inspection. Experimental results on the RailFOD23 dataset demonstrate that YOLOv8-FOD reduces parameter count by 36.6%, model size by 31.1%, and computational complexity (GFLOPs) by 40.7%, while maintaining mAP@0.5 at 94.0% and improving mAP@[0.5:0.95] by 0.3% compared to the baseline. The model achieves a balanced trade-off between accuracy and efficiency, offering a practical solution for real-time intelligent inspection of transmission lines. Yu et al. in their paper presents a multimodal knowledge graph construction method for harmonic analysis in urban power grids, addressing the limitations of existing single-modal (text-only) knowledge graphs that fail to integrate waveform data. The proposed framework adopts a top-down architecture with three interconnected subgraphs covering equipment topology, harmonic detection, and harmonic governance. The resulting knowledge graph, stored and visualized in Neo4j, enables intelligent localization of harmonic problems and retrieval of historical governance schemes, thereby supporting decision-making and improving power quality management in modern grids with high penetration of power electronic devices. Zhu et al. studies a digital twin framework integrated with mixed reality (MR) for online, non-destructive visualization of moisture distribution in the solid insulation of converter transformers. The system achieves a registration accuracy of 2.3 mm and maintains a stable rendering frame rate of ∼90 fps on MR devices, demonstrating its practical feasibility for predictive maintenance and intelligent insulation condition assessment in HVDC applications. Minggui et al. proposes an online rated capacity estimation method for distribution transformers based on multi-sequence impedance fusion, addressing the issues of nameplate vs. actual capacity discrepancies and the need for power outages in conventional detection. The method requires no power interruption, uses only existing measurement infrastructure, and is suitable for capacities below 800 kVA, offering improved robustness and engineering practicality over single-impedance approaches. MAFD-NET, a YOLO11-based network for power tower detection in high-resolution remote sensing images is introduced by Ye et al. It addresses scale variation, background clutter, and small-target miss rates via two novel modules: MAFB, which combines Mamba and multi-head self-attention to capture global and local features while suppressing noise, and EPDU, which performs content-aware dynamic upsampling to preserve edge details of small towers. Ablation studies and visualizations confirm its robustness and practical value for intelligent grid inspection.
The second research area is about Fault Diagnosis, which collected 3 papers. Zenghao et al. in their paper develops TLICMNet, a fast and accurate transmission-line icing detection method tailored for UAV inspection scenarios. To support evaluation, the authors construct a dedicated transmission-line icing dataset (TLICD) and conduct extensive ablation and comparative experiments, effectively balancing high detection precision with real-time performance for practical power grid inspection tasks. Su et al. proposes a TGAN-SATCN model for anomaly detection and fault diagnosis of main steam temperature in thermal power units. To overcome the issues of limited and imbalanced fault samples, the Time-Generative Adversarial Network (TGAN) is used to augment the training data by generating synthetic samples that preserve temporal characteristics. The model effectively detects main steam temperature anomalies and identifies fault causes, thereby enhancing the safety and efficiency of thermal power plant operations. At the end, a fault detection and section localization method for single-phase-to-ground faults in active distribution networks is examined by Yang, addressing the challenge that fault features are easily obscured by random fluctuations from high-penetration distributed generation. Simulations on a 33-bus distribution network under various scenarios—without DG, with DG, and with neutral grounding via arc-suppression coil—demonstrate that the method accurately detects fault timing and locates faulted sections, maintaining high detection rate and localization accuracy even under complex disturbances, high-impedance faults, and parameter uncertainties, showing strong robustness and practical applicability.
The goal of this Research Topic is to offer a systematic overview of recent progress in power system condition monitoring, fault diagnosis, and environmental compatibility. The outcomes are expected to provide meaningful guidance for the protection community, enhancing grid reliability and safety, fostering innovation in diagnostic methods, and supporting the transition toward environmentally sustainable power systems. Looking forward, as intelligent and digital transformation gathers pace, these functions will become increasingly dependent on multi-source data integration and artificial intelligence, evolving from passive detection and alerting to proactive autonomous decision-making. Concurrently, the tighter coupling of environmental stewardship with grid operations will emerge as a defining feature of next-generation systems, steering the power industry toward a future that is cleaner, more efficient, and more resilient.
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Author contributions
DL: Writing – original draft. FL: Writing – review and editing. JC: Writing – review and editing. HW: Writing – review and editing. SY: Writing – review and editing.
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The author(s) declared that financial support was not received for this work and/or its publication.
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The author FL declared that they were an editorial board member of Frontiers at the time of submission. This had no impact on the peer review process and the final decision.
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Summary
Keywords
condition monitor, environmental compatibility, fault diagnosis, power system, relaying protection
Citation
Liu D, Liu F, Chen J, Wan H and Yao S (2026) Editorial: New advancements in power system condition monitoring, fault diagnosis and environmental compatibility. Front. Energy Res. 14:1976807. doi: 10.3389/fenrg.2026.1976807
Received
24 August 2026
Revised
26 August 2026
Accepted
01 September 2026
Published
09 September 2026
Volume
14 - 2026
Edited and reviewed by
ZhaoYang Dong, City University of Hong Kong, Hong Kong SAR, China
Updates
Copyright
© 2026 Liu, Liu, Chen, Wan and Yao.
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) and the copyright owner(s) 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: Feng Liu, f.liu_1@njtech.edu.cn; Jun Chen, 202310007473@njtech.edu.cn
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.