SEMINAR: Applications of Control Theory and Data Science to Improve Distribution System Operation and Cybersecurity

SEMINAR: Applications of Control Theory and Data Science to Improve Distribution System Operation and Cybersecurity

Seminar Abstract 

This talk will discuss three research efforts underway at LBL that utilize tools from nonlinear control and data science to improve how the distribution system is operated and to highlight cybersecurity vulnerabilities and mitigation techniques.  The first part of the talk will focus on the development of a decentralized model-free control strategy for managing Distributed Energy Resources (DER) to provide transmission level services.  The second part of the presentation will highlight efforts to develop a framework that mines high resolution distribution system telemetry to learn utility voltage regulation schemes and infer voltage regulation component health.  The final portion of the talk will discuss very recent efforts aimed at understanding the impact of smart inverter control functions on distribution grid voltages.

 

 

Seminar Speaker(s) 

Daniel Arnold
Energy/Environmental Policy Research Scientist/Engineer, Distribution Grid, Grid Integration Group, Energy Storage & Distributed Resources Division

Daniel Arnold received the B.S. degree in mechanical engineering from the University of California, San Diego, in 2005, the M.S. degree in engineering science from the University of California, San Diego, in 2006. From 2006 to 2009 he was conducted research and development of unmanned underwater vehicles for the United States Navy. He then received his Ph.D. from the Mechanical Engineering Dept. at the University of California, Berkeley in 2015. He is currently a 2015 ITRI-Rosenfeld Postdoctoral Fellow at the Lawrence Berkeley National Laboratory. Presently, his research focuses on the development of control strategies for the management of distributed energy resources in electric power distribution systems. Additionally, he is studying the application of machine learning techniques to analyze high resolution distribution system Phasor Measurement Unit (PMU) data. His research interests include controls, optimization, power systems, and robotics.

Date 

Jun 5, 2017 -
12:00pm to 1:00pm

Location 

90-3122