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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM65364
Volume:
10 |
6 |
Jun 2026
Pages:
1-11
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Law Department
Sanskriti Law College
Extra High Voltage (EHV) transmission lines are essential for bulk power transfer over long distances with improved efficiency and reduced transmission losses. However, the increasing complexity of modern power systems, renewable energy integration, and dynamic load variations create challenges in accurate modeling, monitoring, and simulation of EHV networks. Artificial Intelligence (AI) has emerged as an advanced solution for improving the analysis, prediction, fault detection, and optimization of transmission systems. This research paper explores the role of AI in the modeling and simulation of EHV transmission lines using machine learning, neural networks, deep learning, and digital twin technologies. The study discusses AI-based methods for fault prediction, transient analysis, stability enhancement, predictive maintenance, and electromagnetic field analysis. Simulation approaches using MATLAB/Simulink and AI-integrated frameworks are also examined. The research concludes that AI significantly improves the accuracy, speed, reliability, and automation of EHV transmission system analysis and supports the development of intelligent smart grids.
KEYWORDS
Artificial Intelligence has become a transformative technology in the modeling and simulation of Extra High Voltage transmission lines. AI techniques improve fault detection, predictive maintenance, transient analysis, and real-time monitoring of transmission systems. Compared with conventional mathematical approaches, AI-based models provide faster computation, improved accuracy, and adaptive learning capabilities. The integration of machine learning, deep learning, and digital twin technologies can significantly enhance the efficiency, reliability, and intelligence of future EHV transmission networks. AI-driven smart transmission systems will play a critical role in the development of sustainable and intelligent power grids.
1. Power System Analysis. Power System Analysis. McGraw-Hill Education. 2. Electrical Power Systems. Electrical Power Systems. New Age International Publishers. 3. Power System Stability and Control. Power System Stability and Control. McGraw-Hill. 4. Modern Power System Analysis. Modern Power System Analysis. Tata McGraw-Hill. 5. Ekonomou, L., et al. “High Voltage Transmission Lines Studies with the Use of Artificial Intelligence.” Electric Power Systems Research, Vol. 79, Issue 12, 2009, pp. 1655–1660. 6. Barakou, F., Bollen, M.H.J., Mousavi Gargari, S., Wouters, P.A.A.F., and Steennis, E.F. “Downstream Network Modeling for Switching Transients in EHV Networks Containing Cables.” 2017 IEEE Manchester PowerTech Conference, IEEE, 2017. 7. Meriouma, T., Bessedik, S.A., Djekidel, R., and El-Sehiemy, R.A. “Development of Hybrid Optimization Approach Combined with AI-Based Techniques for Prediction of Electrical Fields in Overhead Transmission Lines.” Journal of Supercomputing, 2025. 8. “Investigation of Necessary Modeling Detail of a Large Scale EHV Transmission Network for Slow Front Transients.” Electric Power Systems Research, Vol. 147, 2017, pp. 192–200. 9. “Simulation Based Analysis of Switching and Lightning Transient Effects on Extra High Voltage AC Overhead Transmission Line.” IEEE Conference Publication, IEEE Xplore. 10. “Modelling of the Transients on the Multi-Circuit EHV/HV Overhead Transmission Lines.” IEEE Conference Publication, IEEE Xplore.
Publication Date
6/24/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
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