Review on Application of digital twins in the electrical power system

Authors

  • Chandrakant Ramesh Shinde Department of Electrical Engineering, Prof. Ram Meghe College of Engineering & Management , Badnera, Amravati , India.
  • Kiran A. Dongre Department of Electrical Engineering, Prof. Ram Meghe College of Engineering & Management , Badnera , Amravati , India.

DOI:

https://doi.org/10.70917/ijcisim-2026-3231

Keywords:

Digital Twin, Electrical Power Systems, Smart Grid, Internet of Things (IoT), Artificial Intelligence, Predictive Maintenance, Renewable Energy Integration

Abstract

Recent years have seen the electrical power system an area of rapid change due to the significant interleaving electric vehicles. This complexity has grown to the point where there is an increasing need for intelligent solutions that can enhance the monitoring, operation and maintenance of the system. The review covers the research done in the past five years and gives a detailed summary of the Digital Twin concept applied in Electrical Power Systems industry. They can be combined to enable a wide range of applications such as real-time monitoring of systems, predictive maintenance, fault diagnostics, asset management, integration with renewable energies, and intelligent decision-making during operation.The reviewed studies prove advantages such as better operation efficiency, higher reliability of the grid, better planning of maintenance, lower operating costs and higher sustainability of the power system infrastructure. However, there are still a number of technical challenges that limit the widespread deployment, such as maintaining high quality data, cyber security, interoperability between different platforms, computational needs, as well as common standards to follow when putting it in place and integrating with legacy systems.

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Published

2026-07-16

How to Cite

Chandrakant Ramesh Shinde, & Kiran A. Dongre. (2026). Review on Application of digital twins in the electrical power system. International Journal of Computer Information Systems and Industrial Management Applications, 18(8s), 242–262. https://doi.org/10.70917/ijcisim-2026-3231

Issue

Section

Original Articles