Development of AI/ML Enabled Digital Twin for EHV 400/220 kV Substation: A Review

Abstract

The increasing complexity of Extra High Voltage (EHV) 400/220 kV substations demands intelligent, automated solutions for asset management, operational efficiency, and predictive maintenance. Traditional maintenance and monitoring systems rely on periodic inspections and static threshold-based alarms, which fail to capture dynamic operational variations and early degradation indicators. To overcome these limitations, this project proposes an AI/ML-enabled Digital Twin framework designed to create a real-time, data-driven virtual replica of the physical substation, enabling continuous situational awareness and proactive maintenance decision-making. The proposed system integrates multi-source data acquired from SCADA, Intelligent Electronic Devices (IEDs), IoT sensors, and condition-monitoring equipment to reflect asset health and operational states accurately. Advanced machine learning algorithms, including regression, classification, and time-series forecasting models, are employed to predict potential faults, estimate the remaining useful life of critical equipment, and optimize maintenance schedules. The digital twin architecture utilizes a hybrid edge–cloud computing environment to ensure low-latency analytics, efficient storage, and scalable processing of high-frequency operational data streams. An interactive visualization interface supports the model, offering dynamic dashboards, predictive analytics, and diagnostic insights for operators. This integration of Artificial Intelligence, Machine Learning, and Digital Twin technologies establishes a comprehensive predictive maintenance ecosystem that reduces downtime, minimizes operational risks, and extends asset lifespan. The proposed framework signifies a major step toward intelligent, self-learning substations and contributes to the realization of resilient, efficient, and autonomous power systems for the next generation of smart grids.

Introduction

The text discusses the transformative role of Artificial Intelligence (AI), Machine Learning (ML), and Digital Twin (DT) technology in modern electrical power systems, with a specific focus on Extra High Voltage (EHV) 400/220 kV substations. These substations are critical assets for grid stability and bulk power transmission but face increasing challenges due to system complexity, aging infrastructure, and renewable energy integration. Digital twins—virtual replicas of physical assets—combined with AI/ML enable real-time monitoring, predictive maintenance, fault diagnosis, and operational optimization, shifting maintenance strategies from reactive to proactive and data-driven approaches.

The proposed digital twin framework integrates data from SCADA systems, intelligent electronic devices (IEDs), sensors, weather sources, and maintenance records to create a real-time virtual representation of substations. AI/ML models support equipment health assessment, fault prediction, remaining useful life estimation, and scenario-based simulation, allowing operators to test decisions virtually without risking physical assets.

The literature review synthesizes findings from four major survey papers. Collectively, these studies highlight that digital twins, enabled by IoT and advanced modeling techniques, are increasingly applied across smart grids, electric machines, energy storage, and predictive maintenance. AI techniques—ranging from traditional machine learning to deep learning, reinforcement learning, and transfer learning—have demonstrated high fault detection accuracy (often 85–95%), reduced unplanned outages (up to 35%), and extended asset lifespans (around 25%). Digital twins also support “what-if” simulations and self-healing grid concepts, enabling faster fault isolation and recovery.

However, several challenges remain, including lack of standardization, data scarcity and imbalance, high computational demands, cybersecurity risks, interoperability issues, and the “black-box” nature of deep learning models. Explainable AI (XAI), lightweight models, transfer learning, and federated learning are identified as promising solutions to improve trust, scalability, and real-world deployment.

A comparative analysis concludes that while all reviewed papers contribute valuable insights, the systematic review on AI-driven fault detection and predictive maintenance (Rana, 2025) is the most relevant for developing an AI/ML-enabled digital twin for EHV substations. This is due to its strong empirical validation, clear demonstration of operational benefits, and direct alignment with predictive maintenance and reliability enhancement goals.

Conclusion

The collective literature illustrates a transformative paradigm shift in power system management, moving from traditional, reactive maintenance to intelligent, data-driven ecosystems. Kabir et al. and Rahmani-Sane et al. establish the foundational conceptual frameworks for this evolution, defining the \"Energy Digital Twin\" not merely as a static simulation, but as a bidirectional, \"living\" replica that continuously synchronizes with physical assets via robust IoT infrastructure. This convergence of technologies enables utilities to transcend passive monitoring, facilitating real-time situational awareness and the seamless integration of complex renewable energy sources that conventional rule-based models are ill-equipped to handle. The technical efficacy of this approach is rigorously validated across both system-wide and component-specific domains within the surveyed texts. Rana (2025) provides compelling empirical evidence for the business case, demonstrating that AI-driven predictive maintenance significantly outperforms legacy methods by reducing unplanned outages by 35% and extending asset lifespans by 25%. Complementing this macro-level view, Hu et al. highlights the precision of Digital Twins at the micro-level, reporting near-perfect diagnostic accuracies (up to 99.99%) for specific electric machine faults through high-fidelity multi-physics modeling. Together, these studies confirm that the synergy of Deep Learning and Digital Twins offers a versatile solution capable of addressing the full spectrum of grid challenges, from specific motor degradation to dynamic grid security assessments. Despite the demonstrated operational benefits, the path toward fully autonomous, \"self-healing\" grids remains obstructed by significant challenges regarding interoperability, data scarcity, and algorithmic transparency. The authors universally identify the \"black-box\" nature of Deep Learning as a barrier to operator trust, advocating for the urgent adoption of Explainable AI (XAI) and standardized data protocols to ensure reliability. Looking forward, the consensus across all four papers points toward a future of \"closed-loop intelligence,\" where Digital Twins evolve from predictive tools into active controllers that can autonomously execute corrective actions, ultimately realizing the vision of a resilient and self-optimizing energy infrastructure.

References

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Copyright

Copyright © 2026 Dr. S Gunasekaran, Ms. Rishika K K, Mohammed Shibil, Navaneeth R, Suhail S, Silvas Kumar S. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.