Conductor Aging Monitoring and Intelligent Analysis Strategies for Power Grid Status Visualization
DOI:
https://doi.org/10.4108/ew.14600Keywords:
Power grid status visualization, Conductor aging monitoring, Cloud-edge-end collaboration, Digital twin, Multi-source data fusionAbstract
INTRODUCTION: Continuous sensing, unified analysis, and spatial representation of conductor aging remain challenging in power-line monitoring involving computer vision, edge computing, and digital twins.
OBJECTIVES: This study develops a cloud-edge collaborative framework for power-grid status visualization to improve conductor defect detection and aging-risk assessment under complex operating conditions.
METHODS: The sensing layer collects visible-light, infrared, electrical, vibration, and micrometeorological data; edge nodes perform time alignment, quality assessment, lightweight defect inference, and event filtering; and the cloud establishes conductor digital-twin instances through object-level coding.
RESULTS: The proposed lightweight network achieves an mAP@0.5 of 96.05% with a single-frame inference time of 16.3 ms. The aging-state recognition accuracy reaches 95.16%, while the health-index mean absolute error is 0.041, the 7-period forecast RMSE is 0.047, and the risk-classification F1 score is 94.12%.
CONCLUSION: Dynamic overlay visualization reduces the anomaly localization time to 34.7 s and increases the task completion rate to 96.5%. Quality-aware fusion and digital-twin object mapping establish a closed-loop connection among multi-source monitoring, state prediction, and 3D interactive decision support.
Downloads
References
[1] Santos T, Cunha T, Dias A, et al. UAV visual and thermographic power line detection using deep learning[J]. Sensors, 2024, 24(17): 5678.
[2] Chatzargyros G, Papakonstantinou A, Kotoula V, et al. Uav inspections of power transmission networks with ai technology: A case study of lesvos island in greece[J]. Energies, 2024, 17(14): 3518.
[3] Bellou E, Pisica I, Banitsas K. Aerial inspection of high-voltage power lines using YOLOv8 real-time object detector[J]. Energies, 2024, 17(11): 2535.
[4] Singh G, Stefenon S F, Yow K C. Interpretable visual transmission lines inspections using pseudo-prototypical part network[J]. Machine Vision and Applications, 2023 (3): 41.
[5] Sharma P, Saurav S, Singh S. Object detection in power line infrastructure: A review of the challenges and solutions[J]. Engineering Applications of Artificial Intelligence, 2024, 130: 107781.
[6] Faisal M A A, Mecheter I, Qiblawey Y, et al. Deep learning in automated power line inspection: A review[J]. Applied Energy, 2025, 385: 125507.
[7] Ukiwe E K, Adeshina S A, Jacob T, et al. Deep learning model for detection of hotspots using infrared thermographic images of electrical installations[J]. Journal of Electrical Systems and Information Technology, 2024, 11(1): 24.
[8] Pulcini V, Modoni G. Machine learning-based digital twin of a conveyor belt for predictive maintenance[J]. The International Journal of Advanced Manufacturing Technology, 2024, 133(11): 6095-6110.
[9] Islam M R, Subramaniam M, Huang P C. Image-based deep learning for smart digital twins: a review[J]. Artificial Intelligence Review, 2025, 58(5): 146.
[10] Bala A, Rashid R Z J A, Ismail I, et al. Artificial intelligence and edge computing for machine maintenance-review[J]. Artificial Intelligence Review, 2024, 57(5): 119.
[11] Jayathilake S, Rajeev P, Gad E. Review of the design and condition monitoring of overhead power distribution conductors[J]. Journal of Civil Engineering and Management, 2025, 31(4): 338-361.
[12] Hou C, Li Z L, Shen X L, et al. Real‐time defect detection method based on YOLO‐GSS at the edge end of a transmission line[J]. IET Image Processing, 2024, 18(5): 1315-1327.
[13] Zeng R, Li Z, Li S, et al. Cloud-Edge Collaboration-Based Data Processing Method for Distribution Terminal Unit Edge Clusters[J]. Energies, 2026, 19(1): 269.
[14] Da Rosa M A, Henrique C M, Bolacell G S, et al. High-voltage measurement infrastructure based on optical technology for transmission lines[J]. Energies, 2025, 18(4): 830.
[15] Vieira e Silva A L B, de Castro Felix H, Simões F P M, et al. Insplad: A dataset and benchmark for power line asset inspection in uav images[J]. International journal of remote sensing, 2023, 44(23): 7294-7320.
[16] Zanelli F, Mauri M, Castelli-Dezza F, et al. Analysis of wind-induced vibrations on HVTL conductors using wireless sensors[J]. Sensors, 2022, 22(21): 8165.
[17] Mchirgui N, Quadar N, Kraiem H, et al. The applications and challenges of digital twin technology in smart grids: A comprehensive review[J]. Applied Sciences, 2024, 14(23): 10933.
[18] Malik M T, Obaid H M, Rasool H F. Integrating digital twin and sensor technologies for future-ready smart grids[J]. Discover Electronics, 2026, 3(1): 42.
[19] Pinciroli Vago N O, Forbicini F, Fraternali P. Predicting machine failures from multivariate time series: An industrial case study[J]. Machines, 2024, 12(6): 357.
[20] Nele L, Mattera G, Yap E W, et al. Towards the application of machine learning in digital twin technology: a multi-scale review[J]. Discover Applied Sciences, 2024, 6(10): 502.
[21] de Albuquerque J M N, Neto O R, Armênio G F, et al. A novel method for multi-modal predictive inspection of power lines[J]. IEEE Access, 2024, 12: 184680-184691.
[22] Das L, Gjorgiev B, Sansavini G. An improved anomaly detection model for automated inspection of power line insulators[J]. Engineering Applications of Artificial Intelligence, 2025, 158: 111431.
[23] Rocha P D, Lopes F J P, Cruz L A D S. Automating electrical grid asset inspection: From current challenges to future directions[J]. IEEE Access, 2025, 13: 201392-201438.
[24] Tunio N A, Tunio M A, Raza M A, et al. Performance comparison between deep learning models for fault classification in transmission lines using time series data[J]. Energy Science & Engineering, 2025, 13(5): 2330-2351.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Shuo Yang, Ao Shen, Yiming Ren, Dianjun Li
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
This is an open-access article distributed under the terms of the Creative Commons Attribution CC BY 4.0 license, which permits unlimited use, distribution, and reproduction in any medium so long as the original work is properly cited.
