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March 26, 2026Discover Electronics4 citationsOpen Access

Integrating digital twin and sensor technologies for future-ready smart grids

MMMaaz Tahir MalikHOHafiz Muhammad ObaidHRHafiz Faiz Rasool

Key Points

  • The aim is to explore how digital twin technology can revolutionize smart grid operations through real-time monitoring and analytics.
  • Developed a layered Digital Twin framework using advanced sensing and data analytics.
  • Applied machine learning techniques for predictive capabilities in power systems.
  • Highlighted key applications of Digital Twin in smart grid environments.
  • Enabled real-time monitoring and analysis of power systems.
  • Enhanced system modeling accuracy and situational awareness.
  • Demonstrated reductions in downtime and improved energy distribution efficiency.

Abstract

Digital Twin technology is rapidly emerging as a transformative force in various industries, playing a pivotal role in the advancement of Industry 4.0. As industries evolve, the implementation of Digital Twin is anticipated to drive significant technological progress across sectors such as manufacturing, smart cities, healthcare, and transportation. This paper presents a layered Digital Twin framework that synthesizes recent advances in sensing, data analytics, and machine learning to enable real-time monitoring, analysis, and predictive capabilities for modern power systems. By leveraging these advanced sensors, the Digital Twin framework enables more accurate system modeling, enhanced situational awareness, and improved decision-making processes. This approach aims to significantly transform traditional smart grid operations by offering a more responsive, resilient, and efficient energy management system, ultimately contributing to the optimization and modernization of existing power infrastructure. Furthermore, by highlighting key smart grid applications, the paper demonstrates the effectiveness of Digital Twin and sensor technology in optimizing power distribution, reducing downtime, and addressing prevalent energy challenges, thereby paving the way for resilient and intelligent power systems.

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Cite This Study

Malik et al. (2026) studied this question.

synapsesocial.com/papers/69c4cdcdfdc3bde44891a95fhttps://doi.org/10.1007/s44291-026-00196-w
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