“21st PSCC 2020 papers submission and review platform

Full Program »

Interleaving Physics- and Data-Driven Models for Power System Transient Dynamics

View File
PDF
0.8MB

The paper explores interleaved and coordinated refinement of physics- and data-driven models in describing transient phenomena in large-scale power systems. We develop and study an integrated analytical and computational data-driven gray box environment needed to achieve this aim. Main ingredients include computational differential geometry-based model reduction, optimization-based compressed sensing, and a finite approximation of the Koopman operator. The proposed two-step procedure (the model reduction by differential geometric (information geometry) tools, and data refinement by the compressed sensing and Koopman theory based dynamics prediction) is illustrated on the multi-machine benchmark example of IEEE 14-bus system with renewable sources, where the results are shown for doubly-fed induction generator (DFIG) with local measurements in the connection point. The algorithm is directly applicable to identification of other dynamic components (for example, dynamic loads).

Author(s):

Alex Stankovic    
Tufts University
United States

Andrija Saric    
Tufts University
United States

Mark Transtrum    
Brigham Young University
United States

Andrija Saric    
University of Novi Sad
Serbia

 

Powered by OpenConf®
Copyright ©2002-2014 Zakon Group LLC