A recent technical article by Sruti Chakraborty, Product Manager at Omicron, published in March edition of Transformer Technology Magazine, highlights the transformative potential of AI-driven digital twins (DT) in the power transformer industry. These virtual replicas of physical assets offer real-time visualization and decision support, bolstering operational efficiency and reliability in the smart grid ecosystem.

The emergence of digital twin (DT) technology in the power sector has garnered attention from grid operators and industry experts alike. As described by Conseil International des Grands Réseaux Electriques (CIGRE), DTs provide dynamic insights into asset management, revolutionizing traditional approaches to design optimization and monitoring.

However, challenges such as errors in scaling down physical assets and complexities in data integration underscore the need for further research and development. CIGRE's joint working group A2/D2.65 aims to bridge this reality gap by providing recommendations for the future development of transformer DT technologies.

An AI-driven transformer digital twin offers unparalleled adaptability and predictive capabilities, facilitating bi-directional communication between physical and virtual assets. Despite the promise, the choice of AI methods must consider the handling of static and non-comparable data, as well as addressing bias-variance trade-offs.

In conclusion, the adoption of transformer DTs represents a significant advancement for smart grid operators. As highlighted by Sruti Chakraborty, leveraging AI-driven solutions can maximize ROI, though decision-makers must weigh the costs and expertise required for customization against the potential benefits.

Explore the transformative potential of AI-driven digital twins in the power transformer industry and read the full article here.