عنوان سخنرانی: Data-Driven Modeling using System Identification Techniques for Enhanced Power System Monitoring and Control
 

System identification techniques are incredibly valuable for power system applications. Such techniques can be used for system-level oscillatory monitoring, plant-level dynamic performance monitoring and control design. This talk describes how the different data-driven techniques developed by the system identification community can be used for such purposes and applied to power systems.  The talk will introduce a framework for designing a multisine probing signal that, when applied in the control inputs of either power electronics-based devices (e.g. HVDC) or conventional synchronous machines, is able to provide a damping estimation with user specified (low) variance. The employed framework is demonstrated through simulations of Modelica-based nonlinear power system models of varying complexity. In addition, when damping performances are insufficient (e.g. an untuned PSS in a generator), we take advantage of the high-quality models identified from pseudo-measurements to re-design controllers that can achieve better performance if deployed.Finally, as the next step to bring such techniques into practice, we demonstrate recent work on prototyping of probing experiments, where power system models designed with Modelica are exported via the FMI standard for deployment in real-time simulators. The portability of the studied model allows not only for off-line design of the identification technique, but also enables testing in the real-time simulator environment for probing signal design optimization before field experiments are conducted.The contents of this presentation are based on work supported by the SuperGrid Institute, Lyon, France; and Dominion Energy Virginia, Richmond, Virginia.

 

Luigi Vanfretti (Senior Member, IEEE) received the M.Sc. and Ph.D. degrees in electric power engineering from the Rensselaer Polytechnic Institute, Troy, NY, USA, in 2007 and 2009, respectively.He is currently Visiting Faculty at Mitsubishi Electric Research Laboratories (MERL) in Cambridge, MA, working with the Multi-Physical Modeling research team on grid-interactive building energy system design and control.Since July 2022, he is a Full Professor at Rensselaer Polytechnic Institute, where he was a tenured Associate Professor from 2017-6/2022-6. At RPI, he leads research projects in his laboratory and with his research team, ALSETLab, in the domains of electrical power systems and aircraft electrification.Professor Vanfretti has held temporary posts in prestigious international institutions, in 2019 he was a Visiting Faculty at the King Abdullah University of Science and Technology, Thuwal, Saudi Arabia; and in 2022 he was a Visiting Professor at both the Laboratoire Ampère of the École Centrale de Lyon and the SuperGrid Institute, both located in Lyon, France.In addition to his academic duties, through Vanfretti Consulting LLC, Dr. Vanfretti serves as a consultant for utilities and research institutions, including Dominion Energy in VA, the SuperGrid Institute in Lyon, France, and CENACE (the power system operator of Ecuador), Quito, Ecuador, to name a few.Prof. Vanfretti was an Assistant Professor,  Associate Professor (tenured) and a Docent with the KTH Royal Institute of Technology, Stockholm, Sweden, from 2010 to 2013 and from 2013 to 2017, respectively, where he led the SmarTS Lab (a research group). He was with Statnett SF, the Norwegian transmission system operator, from 2013 to 2016, as a Special Advisor with the Research and Development Department and as a Consultant, from 2011 to 2012 and in 2017.His research interests are in “electrified systems,”' and include cyber-physical system (CPS) modeling, simulation, stability and control in the domains of power grid and electrified transportation, both vehicles and infrastructure. In addition, he performs research on synchrophasor technologies, both on technology development and data analytics, and is interested in the application of software technologies, signal processing, system identification and machine learning for design and operation analytics for CPS.

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