Data-Centric Power System Analysis with Generative Models

#Power_System_Analysis #GAN #LLM #WIE
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Modern power systems increasingly rely on data-driven tools for load analysis, monitoring, and operational decision-making. However, utilities and system operators often face practical challenges such as limited data availability, missing measurements, privacy constraints, and difficulty capturing rare but critical events. These issues directly limit the effectiveness of advanced analytics in real-world deployments. This talk presents a series of generative and data-driven methods developed to address these challenges using real utility data. First, MultiLoad-GAN is introduced to generate realistic synthetic load profiles that preserve temporal behavior and customer-level diversity, enabling large-scale studies without exposing sensitive data. Second, BERT-PIN and related language-model-based approaches are demonstrated for restoring missing smart meter data, improving data quality for downstream tasks such as load forecasting and planning. Third, the application of pre-trained large language models (LLMs) is discussed, showing how they can be adapted for power system time-series problems with reduced data and training requirements. Finally, a VAE–GAN framework is presented for event detection and classification using synchrophasor data, enabling early identification of abnormal system behavior.  

This presentation will count for 1 Professional Development Hour (PDH) for the PE License in Wisconsin and Michigan.


  Date and Time

  Location

  Hosts

  Registration


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  • 5 N. Systems Drive
  • Appleton, Wisconsin
  • United States 54914
  • Building: D.J. Bordini Center at FVTC
  • Room Number: BC112A
  • Contact Event Hosts

  • Reservations should be received by Tuesday, February 3 by registering from this web page.

    The Dinner fee of $20, will be collected at registration.

    IEEE Student Members may attend and enjoy dinner at the reduced cost of $10.  Student members should register by emailing their IEEE member number to blluchs@ieee.org or oliveira@mtu.edu.

  • Starts 20 January 2026 07:00 PM UTC
  • Ends 04 February 2026 03:00 AM UTC
  • Admission fee ?

  Speakers

Dr. Yi Hu of Michigan Technological University

Biography:


   

Yi Hu is an Assistant Professor in the Department of Electrical and Computer Engineering at Michigan Technological University. He earned his Ph.D. in Electrical Engineering from North Carolina State University. He also holds an M.S. degree from Peking University and a B.S. degree from Chongqing University of Posts and Telecommunications, both in Electrical Engineering. His research interests include machine learning and data analytics in power systems, such as synthetic data generation, load modeling, and reliability analysis.

 




Agenda

5:00 Featured Speaker - Dr. Yi Hu

        D.J. Bordini Center at FVTC
       

       

6:30 pm CST   Social-Happy Hour at Cheddar's Scratch Kitchen
                        4531 W Wisconsin Ave
                        Appleton, WI

6:45 pm CST   Dinner at Cheddar's Scratch Kitchen
                        Cost: $20, payable at registration 

7:00 pm CST   Short business meeting
                        Door prize drawing