David Lubkeman

Biography

Lubkeman holds a Ph.D. in electrical engineering from Purdue University with an emphasis in power systems engineering. He has a MS and BS degrees in electrical engineering, also from Purdue.

Lubkeman has more than 25 years of experience in distribution systems and automation and has been an active participant in technical development activities, resulting in more than 40 publications and 13 US patents. Hi sprevious industry experience includes working at Sensus as a senior product manager for distribution automation; at KEMA consulting in the areas of smart grid business case analysus, large-scale energy storage and renewable energy integration; and at ABB, where he was involved in the development of solutions for distribution automation and asset management. Lubkeman's prior academic experience was as an associate professor in the Holcombe Department of Electrical and Computer Engineering at Clemson University. He is also a licensed professional engineer.

Lubkeman is currently a lead instructor with the Elecitrcal Power Systems Engineering (EPSE) Master of Science professional degree program. He also is a research faculty member associated with the NSF FREEDM Systems Center.

Education

• Ph.D. 1983
  Electrical Engineering
  Purdue University, West Lafayette

Recent Publications

• Under-Frequency Load Shedding for Power Reserve Management in Islanded Microgrids (2024)
• A Secure and Adaptive Hierarchical Multi-Timescale Framework for Resilient Load Restoration Using a Community Microgrid (2023)
• A Data-driven Pivot-point-based Time-series Feeder Load Disaggregation Method (2021)
• A Load Switching Group based Feeder-level Microgrid Energy Management Algorithm for Service Restoration in Power Distribution System (2021)
• Agent-Based Modeling of Feeder-Level Electric Vehicle Diffusion for Distribution Planning (2021)
• Diesel Generator Model Parameterization for Microgrid Simulation Using Hybrid Box-Constrained Levenberg-Marquardt Algorithm (2021)
• FeederGAN: Synthetic Feeder Generation via Deep Graph Adversarial Nets (2021)
• Hierarchical Multi-timescale Framework For Operation of Dynamic Community Microgrid (2021)
• Secondary Voltage and Frequency Regulation for Grid Re-Synchronization in Microgrid with Unified Virtual Oscillator Controlled Multi-port Converters (2021)
• FeederGAN: Synthetic Feeder Generation via Deep Graph Adversarial Nets (2020)