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

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, selflearning substations and contributes to the realization of resilient, efficient, and autonomous power systems for the next generation of smart grids