Digital Twin of a Three-Phase Transmission Line with Integrated Fire and Fault Monitoring in MATLAB/Simulink: A Review

Trishanku; Ashish Dewangan; Harsh Marcus

doi:10.26706/ijceae.7.1.20260106

Abstract

Digital twins (DTs) have the potential to deliver modern power system virtual models that replicate system behaviour in real time, including system health and environmental interactions. This review explores DT applications in power transmission and distribution networks, with a focus on fault detection, predictive maintenance, and fire-risk monitoring. The paper reviews research from 2019–2025, covering modelling techniques, data-driven approaches, and implementation challenges. Significant advancements include real-time simulation, sensor integration, deep-learning assisted diagnostics, intelligent protection schemes, and fire-risk monitoring. Despite progress, critical research gaps remain, particularly in multi-domain DTs, environmental integration, and line-level digital twin frameworks. This review outlines future research directions toward intelligent, safe, and resilient power system infrastructure.

Keywords

Digital Twin DT Modelling Transmission Line Power System Fault Analysis Fire Hazard Smart Grid Virtual Replication System Health DT Monitoring

References

M. G. Khan, R. Patel, and A. Singh, “AI-driven digital twin framework for proactive grid condition monitoring,” Journal of Smart Energy Systems, 2025.
F. Rahman and L. Zhou, “Environment-integrated digital twins for climate-resilient power transmission,” International Journal of Energy Informatics, 2025.
Y. Lee, K. Chen, and H. Lam, “Digital twin-enabled wildfire and thermal hotspot prediction for transmission corridors,” Energy Systems Intelligence, 2025.
R. Murthy, S. Das, and P. Varma, “Multi-physics digital twin modelling for weather-induced stress on power networks,” Smart Grid Simulation Review, 2025.
E. Cortes and I. Bhandari, “Virtual-sensor-supported digital twin for predictive transmission line maintenance,” Journal of Grid Analytics, 2025.
J. Zhao, P. Li, and M. Yang, “Panoramic digital twin-based substation monitoring for early failure detection,” Electrical Systems Safety Journal, 2025.
W. Hu, Q. Zhang, and Y. Luo, “Digital twin-driven fault diagnosis for electrical machines: A comprehensive review,” Sensors and Intelligent Diagnostics, 2025.
A. Mendhiratta, K. Roy, and N. Prasad, “Graph-based digital twins for enhanced grid anomaly detection,” in Proc. Int. Conf. Computational Science, 2025.
X. Tang, H. Zhou, and J. Park, “Semi-distributed digital twin network for large-scale fault diagnosis,” IEEE Trans. Networked Systems, 2025.
N. Thwe, H. Aung, and T. Tun, “Digital twins for power systems: Practices, architectures, and challenges,” Energy Digitalization Review, 2024.
L. Heluany, A. Martins, and P. Silva, “Digital twin adoption in power generation and modern energy systems,” Journal of Sustainable Energy Engineering, vol. 12, no. 3, pp. 189–204, 2024, doi: 10.1080/14786451.2024.2301154.
Y. Yin, L. Jin, and H. Wu, “Digital twin-driven fault situation identification in distribution networks,” Electric Power Systems Research, vol. 224, p. 109592, 2024, doi: 10.1016/j.epsr.2023.109592.
Q. Zhao, X. Chen, and W. Liu, “Panoramic digital twin for substation fault monitoring and equipment health detection,” IEEE Access, vol. 12, pp. 23841–23855, 2024, doi: 10.1109/ACCESS.2024.3367812.
S. Hu, J. Li, and R. Chao, “Digital twin-enabled fault diagnosis of electric machines using machine learning,” Machines, vol. 12, no. 1, 2024, doi: 10.3390/machines12010045.
THESAI Group, “Power line fault detection combining deep learning and digital twin modelling,” Int. J. Advanced Computer Science and Applications, vol. 15, no. 4, pp. 312–320, 2024, doi: 10.14569/IJACSA.2024.0150437.
A. Mendhiratta, S. Kumar, and N. Tripathi, “Graph-oriented digital twin for real-time grid anomaly detection,” in Proc. Smart Energy Systems Conf., 2024.
L. Tang, H. Wei, and Z. Wang, “Distributed digital twin architecture for network-level fault detection,” in Proc. IEEE Computer Society, 2024.
International Electrotechnical Commission, Virtualizing Power Systems Using Digital Twin Technologies for Risk Mitigation, IEC White Paper, Geneva, Switzerland, 2024.
A. Yassin, H. Omar, and M. El-Sayed, “Digital twin in power system research and development: Principles, scope, and challenges,” Energy Systems, vol. 14, no. 2, pp. 421–445, 2023, doi: 10.1007/s12667-022-00529-6.
J. Song, D. Kim, and S. Park, “System-of-digital-twin systems: A scalable architecture for coordinated power system modelling,” Sustainability, vol. 15, no. 3, 2023, doi: 10.3390/su15031984.
D. Han, Q. Liu, and S. Hu, “Digital twin operational structures for active distribution networks,” Renewable Energy, vol. 205, pp. 820–833, 2023, doi: 10.1016/j.renene.2023.01.094.
F. Yang, L. Hao, and X. Sun, “Twin-reality digital twin for transmission line inspection and structural monitoring,” Journal of Modern Information Systems, 2023.
O. Adedokun, R. Patel, and M. Qureshi, “Digital twin modelling for real-time monitoring and fault detection in smart substations,” International Journal of Industrial Engineering Research and Development, 2023.
T. Biard, M. Raymond, and S. Pelletier, “Reliability assessment of electrical networks using digital twin techniques,” Energy Reliability Journal, vol. 9, no. 2, pp. 101–118, 2022.
G. Lamagna, M. Rossi, and T. Walter, “Digital twins for smart energy systems: Architecture and barriers,” Smart Grid Technology Review, vol. 6, no. 1, pp. 44–58, 2021.
R. Da Silva, C. Mendes, and R. Ortiz, “A digital twin approach for fault diagnosis in distributed photovoltaic systems,” Journal of Renewable Power Engineering, vol. 11, no. 4, pp. 355–364, 2019.

[1] M. G. Khan, R. Patel, and A. Singh, “AI-driven digital twin framework for proactive grid condition monitoring,” Journal of Smart Energy Systems, 2025.

[2] F. Rahman and L. Zhou, “Environment-integrated digital twins for climate-resilient power transmission,” International Journal of Energy Informatics, 2025.

[3] Y. Lee, K. Chen, and H. Lam, “Digital twin-enabled wildfire and thermal hotspot prediction for transmission corridors,” Energy Systems Intelligence, 2025.

[4] R. Murthy, S. Das, and P. Varma, “Multi-physics digital twin modelling for weather-induced stress on power networks,” Smart Grid Simulation Review, 2025.

[5] E. Cortes and I. Bhandari, “Virtual-sensor-supported digital twin for predictive transmission line maintenance,” Journal of Grid Analytics, 2025.

[6] J. Zhao, P. Li, and M. Yang, “Panoramic digital twin-based substation monitoring for early failure detection,” Electrical Systems Safety Journal, 2025.

[7] W. Hu, Q. Zhang, and Y. Luo, “Digital twin-driven fault diagnosis for electrical machines: A comprehensive review,” Sensors and Intelligent Diagnostics, 2025.

[8] A. Mendhiratta, K. Roy, and N. Prasad, “Graph-based digital twins for enhanced grid anomaly detection,” in Proc. Int. Conf. Computational Science, 2025.

[9] X. Tang, H. Zhou, and J. Park, “Semi-distributed digital twin network for large-scale fault diagnosis,” IEEE Trans. Networked Systems, 2025.

[10] N. Thwe, H. Aung, and T. Tun, “Digital twins for power systems: Practices, architectures, and challenges,” Energy Digitalization Review, 2024.

[11] L. Heluany, A. Martins, and P. Silva, “Digital twin adoption in power generation and modern energy systems,” Journal of Sustainable Energy Engineering, vol. 12, no. 3, pp. 189–204, 2024, doi: 10.1080/14786451.2024.2301154.

[12] Y. Yin, L. Jin, and H. Wu, “Digital twin-driven fault situation identification in distribution networks,” Electric Power Systems Research, vol. 224, p. 109592, 2024, doi: 10.1016/j.epsr.2023.109592.

[13] Q. Zhao, X. Chen, and W. Liu, “Panoramic digital twin for substation fault monitoring and equipment health detection,” IEEE Access, vol. 12, pp. 23841–23855, 2024, doi: 10.1109/ACCESS.2024.3367812.

[14] S. Hu, J. Li, and R. Chao, “Digital twin-enabled fault diagnosis of electric machines using machine learning,” Machines, vol. 12, no. 1, 2024, doi: 10.3390/machines12010045.

[15] THESAI Group, “Power line fault detection combining deep learning and digital twin modelling,” Int. J. Advanced Computer Science and Applications, vol. 15, no. 4, pp. 312–320, 2024, doi: 10.14569/IJACSA.2024.0150437.

[16] A. Mendhiratta, S. Kumar, and N. Tripathi, “Graph-oriented digital twin for real-time grid anomaly detection,” in Proc. Smart Energy Systems Conf., 2024.

[17] L. Tang, H. Wei, and Z. Wang, “Distributed digital twin architecture for network-level fault detection,” in Proc. IEEE Computer Society, 2024.

[18] International Electrotechnical Commission, Virtualizing Power Systems Using Digital Twin Technologies for Risk Mitigation, IEC White Paper, Geneva, Switzerland, 2024.

[19] A. Yassin, H. Omar, and M. El-Sayed, “Digital twin in power system research and development: Principles, scope, and challenges,” Energy Systems, vol. 14, no. 2, pp. 421–445, 2023, doi: 10.1007/s12667-022-00529-6.

[20] J. Song, D. Kim, and S. Park, “System-of-digital-twin systems: A scalable architecture for coordinated power system modelling,” Sustainability, vol. 15, no. 3, 2023, doi: 10.3390/su15031984.

[21] D. Han, Q. Liu, and S. Hu, “Digital twin operational structures for active distribution networks,” Renewable Energy, vol. 205, pp. 820–833, 2023, doi: 10.1016/j.renene.2023.01.094.

[22] F. Yang, L. Hao, and X. Sun, “Twin-reality digital twin for transmission line inspection and structural monitoring,” Journal of Modern Information Systems, 2023.

[23] O. Adedokun, R. Patel, and M. Qureshi, “Digital twin modelling for real-time monitoring and fault detection in smart substations,” International Journal of Industrial Engineering Research and Development, 2023.

[24] T. Biard, M. Raymond, and S. Pelletier, “Reliability assessment of electrical networks using digital twin techniques,” Energy Reliability Journal, vol. 9, no. 2, pp. 101–118, 2022.

[25] G. Lamagna, M. Rossi, and T. Walter, “Digital twins for smart energy systems: Architecture and barriers,” Smart Grid Technology Review, vol. 6, no. 1, pp. 44–58, 2021.

[26] R. Da Silva, C. Mendes, and R. Ortiz, “A digital twin approach for fault diagnosis in distributed photovoltaic systems,” Journal of Renewable Power Engineering, vol. 11, no. 4, pp. 355–364, 2019.