Connecticut IEEE Power Electronics Society and Power and Energy Society Joint Holiday Party

Bldg: World of Beer, West Hartford, 73 Isham Rd, #B-30, West Hartford, Connecticut, United States, 06107

THE POWER OF CT INDUSTRY! The newly-established Connecticut chapter of the IEEE Power Electronics Society (PELS) and the IEEE Power & Energy Society (PES) chapter invite you to their 2023 social event!!! Please join us and a full cast of Connecticut industrial and academic stakeholders to raise a glass, grab a bite, and chat as we send '23 into the history books. Come mingle with the major players of all corners of CT industry and even beyond, including the likes of Sikorsky, Lockheed Martin, Otis Elevator Company, Collins Aerospace, Pratt & Whitney, Raytheon, Triumph Group, Eversource, General Dynamics/Electric Boat, and academic luminaries from UConn, Central Connecticut State University, and many more from the region (including greater New England and New York). Thanks to the co-sponsors, this is a purely social event (no tech talk) so everything will be at no cost to attendees! This means space is limited and though free, you must pre-register to attend. We respectfully request you cancel your registration if plans change to free the slot for another guest to this exclusive event. Bldg: World of Beer, West Hartford, 73 Isham Rd, #B-30, West Hartford, Connecticut, United States, 06107

Artificial Intelligence-Based Fault Detection and Localization for Underground Cables


Traditional signal detection methods require an explicit prescription of partial discharge features that characterize the state of the cable system. When the sample size is sufficiently large, deep learning models allow complex interrelations of auto-generated features. A distinct challenge is the characterization of the waveform signal, which depends on cable length. In this webinar, an overview of the developed deep learning models for an extensive partial discharge dataset to automate the detection of underground cable defects will be provided. The developed deep learning models outperform predictions with traditional methods. In addition to classifying partial discharge signals, the models identify source locations of the defects within a cable system through recurrent Neural Networks. Additional assessments include advanced data augmentation strategies and interpretability to verify the potential use of the model for predictive maintenance. Co-sponsored by: Power and Energy Systems Research Laboratory Speaker(s): Steffen Ziegler Virtual: