Unlocked: DGA Course – e-lesson #11: A brief “history of the future” – an advanced approach to DGA diagnosis based on AI

This is the eleventh lesson in the DGA course, authored and conducted by Marius Grisaru. Her you can save your seat.

This lesson provides an in-depth exploration of the history and future of Dissolved Gas Analysis (DGA) for transformer diagnostics. The presenter, Marius Grisaru, delves into the evolution of transformer technology, highlighting key inventors and developments that have shaped the industry. He examines the role of insulating oils, their chemical composition, and how changes over time have impacted DGA methods. The lesson covers various DGA diagnosis approaches, from predictive to artificial intelligence-based techniques, discussing their strengths, limitations, and the need for continuous improvement. Marius emphasizes the importance of local evaluation and the challenges posed by the lack of shared operational data, underscoring the importance of collaboration and updating standards. Overall, this lesson offers a comprehensive understanding of the past, present, and future of DGA in transformer health monitoring.

In this lesson we will hear about what the author calls “a brief history of the future”, based on Keynote speech from IEEMA 2018; advanced approach to DGA diagnosis based on AI and ML.

Main takeaways:

1. A brief history of DGA: theoretical reviews of DGA history and the impact on present DGA state
2. Future perspective of DGA alternative methodologies for transformer maintenance