ROSEI Summit for Sustainable Energy Research at JHU

I gave a talk "Learning coherent clusters for modeling and analysis of power system dynamics"(Min & Mallada, 2023; Min & Mallada, 2023) at ROSEI Summit for Sustainable Energy Research at Johns Hopkins University.

Reference

1. 

   Learning Coherent Clusters in Weakly-Connected Network Systems

   Hancheng Min, and Enrique Mallada

   In Proceedings of The 5th Annual Learning for Dynamics and Control Conference, Jun 2023

   Abs arXiv PDF

   We propose a structure-preserving model-reduction methodology for large-scale dynamic networks with tightly-connected components. First, the coherent groups are identified by a spectral clustering algorithm on the graph Laplacian matrix that models the network feedback. Then, a reduced network is built, where each node represents the aggregate dynamics of each coherent group, and the reduced network captures the dynamic coupling between the groups. We provide an upper bound on the approximation error when the network graph is randomly generated from a weight stochastic block model. Finally, numerical experiments align with and validate our theoretical findings.

2. 

   Spectral clustering and model reduction for weakly-connected coherent network systems

   Hancheng Min, and Enrique Mallada

   In 2023 American Control Conference (ACC), Jun 2023

   Abs arXiv PDF

   We propose a novel model-reduction methodology for large-scale dynamic networks with tightly-connected components. First, the coherent groups are identified by a spectral clustering algorithm on the graph Laplacian matrix that models the network feedback. Then, a reduced network is built, where each node represents the aggregate dynamics of each coherent group, and the reduced network captures the dynamic coupling between the groups. Our approach is theoretically justified under a random graph setting. Finally, numerical experiments align with and validate our theoretical findings.