In this presentation, we will explore how San Diego Gas & Electric (SDG&E) is transforming its electric distribution network assets through an innovative integration of digital twin, time-series data, machine learning, and physical asset models. 

Traditionally, utilities have relied on a reactive approach, leading to forced outages and higher operational costs. SDG&E's novel methodology shifts this paradigm by predicting equipment failures and enabling just-in-time replacements, significantly enhancing system reliability and customer satisfaction. Digital Twin plays a crucial role in this transformation by creating virtual replicas of physical grid assets, allowing for real-time monitoring, analysis, simulation, and prediction. By leveraging time-synchronized measurements in conjunction with advanced machine learning algorithms, SDG&E aims to optimize operational efficiencies, reduce costs, and bolster grid resiliency.

Key Takeaways:

• Utilizing time-synchronized measurements innovatively with machine learning technology
• Applying digital twin for primary circuit models to accurately predict equipment failure locations and issue proactive replacement warnings
• Demonstrating the benefits of reducing and managing replacement times, improving crew scheduling, and minimizing collateral damage.

Please join us to discover how SDG&E's cutting-edge approach, incorporating digital twins, is setting a new standard for utility grid management, ensuring a more reliable and efficient energy future.