Application of Digital Twin Technology in Substations: High Voltage Reactor Meter Visual Field Analysis of Phase A
DOI:
https://doi.org/10.54691/gq6tpc49Keywords:
Digital Twin; Substation; High Voltage Reactor Meter; Visual Field Analysis; Predictive Maintenance.Abstract
This paper explores the application of Digital Twin (DT) technology in the monitoring and maintenance of high-voltage reactor meters within substations. Traditional inspection methods face limitations in covering the extensive areas and complex structures within substations, which DT technology can address by providing real-time monitoring, predictive analytics, and data-driven insights. The study focuses on visual field analysis for optimizing camera placement and configuration around high-voltage reactors, enhancing inspection accuracy and operational efficiency. Key components include IoT sensors, real-time analytics, and advanced visualization techniques. The findings suggest that DT-enabled systems significantly improve the reliability, safety, and efficiency of substation management, demonstrating potential for broader smart grid applications.
Downloads
References
[1] Ibrahim, A.; Stefano, L.; Mohammad, S., Towards A Distributed Digital Twin Framework for Predictive Maintenance in Manufacturing Systems. 2024.
[2] Zheng, L.; Erik, P. B.; Min, L.; Chunsheng, Y.; Kazuhiko, T.; Norbert, M. In Digital twin for predictive maintenance, 2023.
[3] Mohanraj, E.; Eniyavan, N.; Sidarth, S.; Shankar, S., Digital Twins for Automotive Predictive Maintenance. 2024.
[4] Rochdi, K.; Safa, K.; Anis, M.; Kais, B., Digital Twin applied to Predictive Maintenance for Industry 4.0. Journal of nondestructive evaluation, diagnostics and prognostics of engineering systems 2024, p. 1-24.
[5] Valerio, P.; Gianfranco, E. M., Machine learning-based digital twin of a conveyor belt for predictive maintenance. The International Journal of Advanced Manufacturing Technology 2024.
[6] Mubarak, A.; Mebrahitom, A.; Azmir, A.; Tamiru, A.; Freselam, M.; Kushendarsyah, S. In Digital Twin Enabled Industry 4.0 Predictive Maintenance Under Reliability-Centred Strategy, 2022; p. 01-06.
[7] Mustafa Furkan, S.; Cengiz, G.; Gokhan, I., Predictive Maintenance Framework for Production Environments Using Digital Twin. 2021, p. 455-462.
[8] Yongmei, L.; Wenjie, C.; ChangQi, L.; Ming, C., Digital Twin and Data-Driven Quality Prediction of Complex Die-Casting Manufacturing. IEEE Transactions on Industrial Informatics 2022, 18, p. 8119-8128.
Downloads
Published
Issue
Section
License
Copyright (c) 2024 Scientific Journal of Intelligent Systems Research

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.




