International Journal of Computational and Electronics Aspects in Engineering
Volume 7 · Issue 1 · February 2026 · pp. 66-72
Review Article · Peer Reviewed
Received: [Received Date] · Accepted: [Accepted Date] · Published: 06 February 2026
DOI: 10.26706/ijceae.7.1.20260106
Open Access · CC BY 4.0

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

Trishanku*, Ashish Dewangan1,2, Harsh Marcus3
1,2Electrical Engineering Department, Christian College of Engineering and Technology, Bhilai, India.
3Computer Science and Engineering, Joginpally Bhaskar Rao Engineering College, Hyderabad, India.

*Corresponding author: shankukumar170@gmail.com
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Abstract

Digital twins (DTs) have the potential to provide modern power systems with virtual models that replicate system behavior in real time, including system health and interactions with the surrounding environment. This review explores the applications of DTs in power transmission and distribution networks, with particular emphasis on fault detection, predictive maintenance, and fire-risk monitoring. The study reviews a broad range of research published during 2019–2025, highlighting recent trends in modeling, data-driven techniques, and challenges associated with practical implementation. Significant developments include real-time simulation, sensor and modern communication integration, deep-learning-assisted diagnostics, intelligent protection schemes, digital twins for electric utility monitoring, and fire-risk assessment. Despite these advances, critical research gaps remain in the development of multi-domain DTs, particularly due to the limited integration of environmental and fire-related parameters and the absence of line-level digital twin frameworks for early hazard detection. The reviewed studies indicate promising future directions toward digital twins capable of integrating electrical behavior with environmental factors and intelligent decision-making mechanisms. This review aims to support researchers and practitioners in developing safer, intelligent, and more reliable 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

  1. L. Zhang, M. Li, and K. Wang, “Internet of Vehicles: Architecture, protocols, and applications,” IEEE Communications Surveys & Tutorials, vol. 26, no. 2, pp. 1045–1078, Second Quarter 2024.
  2. Automotive Edge Computing Consortium, “Connected Vehicle Data Processing Requirements and Infrastructure Analysis,” Tech. Rep. AECC-TR-001, 2024.
  3. X. Wang, Y. Liu, and Z. Chen, “Security for the Internet of Vehicles with integration of sensing, communication, computing, and intelligence: A comprehensive survey,” Sensors, vol. 24, no. 18, article 6125, Sep. 2024.
  4. H. Chen, X. Liu, and C. Martinez, “Real-time cryptographic processing requirements for autonomous vehicle safety systems,” IEEE Transactions on Intelligent Transportation Systems, vol. 25, no. 7, pp. 6789–6801, Jul. 2024.
  5. Intel Corporation, “Intel Core i5-1035G1 processor technical specifications and performance analysis,” Intel Tech. Doc. 338848-001, 2024.
  6. M. H. Alkhalidi, H. S. Maghdid, and S. R. M. Zeebaree, “Cybersecurity in autonomous vehicles—Are we ready for the challenge?” Electronics, vol. 13, no. 13, article 2654, Jul. 2024.
  7. E. C. P. Neto, H. Taslimasa, S. Dadkhah, S. Iqbal, P. Xiong, T. Rahman, and A. A. Ghorbani, “CICIoV2024: Advancing realistic IDS approaches against DoS and spoofing attack in IoV CAN bus,” Internet of Things, vol. 26, article 101209, Jul. 2024.
  8. M. Thompson, J. Davis, and L. Wilson, “Reproducibility crisis in cryptographic research: Challenges and solutions,” IEEE Security & Privacy, vol. 22, no. 3, pp. 45–53, May–Jun. 2024.
  9. B. Rodriguez and K. Kim, “Modern software frameworks for cryptographic implementation and validation,” ACM Computing Surveys, vol. 56, no. 8, article 201, Aug. 2024.
  10. A. Ahmed, B. Hassan, and K. Mahmood, “Performance analysis of hybrid cryptosystems for secure e-services: An empirical study,” International Journal of Information Security, vol. 23, no. 4, pp. 721–738, Jul. 2024.
  11. W. Thompson, J. Lee, and S. Park, “Cross-platform cryptographic algorithm performance evaluation,” ACM Transactions on Information and System Security, vol. 27, no. 2, article 18, Mar. 2024.
  12. D. J. Bernstein and T. Lange, “ECRYPT II benchmarking of cryptographic systems: Final report and analysis,” European Network of Excellence in Cryptology II, Final Report ECRYPT-NOE-2024-01, 2024.
  13. P. Kumar and R. Singh, “Comparative analysis of ECC and RSA for IoT devices: Performance and security considerations,” IEEE Internet of Things Journal, vol. 11, no. 12, pp. 20345–20358, Jun. 2024.
  14. S. Alshammari and A. El-Sayed, “Advancing vehicle security: Deep learning-based solution for defending CAN networks in the Internet of Vehicles,” EAI Endorsed Transactions on Internet of Things, vol. 10, article e3, Oct. 2024.
  15. T. Yang and C. Lv, “A secure sensor fusion framework for connected and automated vehicles under sensor attacks,” IEEE Internet of Things Journal, vol. 9, no. 22, pp. 22357–22365, Nov. 2022.
  16. X. Zhao, Y. Fang, H. Min, X. Wu, W. Wang, and R. Teixeira, “Potential sources of sensor data anomalies for autonomous vehicles: An overview from road vehicle safety perspective,” Expert Systems with Applications, vol. 236, article 121358, Feb. 2024.
  17. M. A. Setitra and M. Fan, “Detection of DDoS attacks in SDN-based VANET using optimized TabNet,” Computer Standards & Interfaces, vol. 89, article 103845, Apr. 2024.
  18. P. Rani, R. Sharma, P. K. Sharma, and V. Chamola, “Federated learning-based misbehaviour detection for the 5G-enabled Internet of Vehicles,” IEEE Transactions on Consumer Electronics, doi: 10.1109/TCE.2023.3328020, 2023.
  19. United Nations Economic Commission for Europe, “UN Regulation No. 155 – Uniform provisions concerning the approval of vehicles with regards to cyber security and cyber security management system,” 2021, updated 2024.
  20. National Institute of Standards and Technology, “Security and privacy controls for information systems and organizations,” NIST Special Publication 800-53 Rev. 5, updated Sep. 2024.
  21. SAE International, “Cybersecurity guidebook for cyber-physical vehicle systems,” SAE J3061, updated Jan. 2024.
  22. International Electrotechnical Commission, “Security for industrial automation and control systems – Part 4-2: Technical security requirements for IACS components,” IEC 62443-4-2, 2024.
  23. M. Fu, P. Wang, M. Liu, Z. Zhang, and X. Zhou, “IoV-BERT-IDS: Hybrid network intrusion detection system in IoV using large language models,” IEEE Transactions on Vehicular Technology, doi: 10.1109/TVT.2024.3402366, 2024.
  24. T. Alladi, B. Gera, A. Agrawal, V. Chamola, and F. R. Yu, “DeepADV: A deep neural network framework for anomaly detection in VANETs,” IEEE Transactions on Vehicular Technology, vol. 70, no. 11, pp. 12013–12023, Nov. 2021.
  25. P. Rani, R. Sharma, and P. K. Sharma, “Federated learning-based security solutions for IoV networks: Recent advances and open issues,” Sensors, vol. 24, no. 2, article 368, Jan. 2024.
  26. E. Khezri, H. Hassanzadeh, R. O. Yahya, and M. Ould-Khaoua, “Security challenges in Internet of Vehicles (IoV) for ITS: A survey,” Tsinghua Science and Technology, vol. 30, no. 4, pp. 1700–1723, Mar. 2025.
  27. Python Software Foundation, “Python 3.13.0 release notes and performance improvements,” Python Enhancement Proposal 3147, Oct. 2024.