38 citations · 38 across the 5 of their papers we have counts for
4 papers · 1 filter
Admittance Sensitivity-Informed Modular GP for Scalable Topology-Adaptive Power-Flow Learning
Henrique O. Caetano, Carlos Dias Maciel, Rahul K. Gupta
Data-driven approaches for learning power flow models suffer from weak generalization across varying network topologies and limited computational scalability. Existing methods typi…
bayesgrid: An Open-Source Python Tool for Generating Probabilistic Synthetic Transmission-Distribution Grids Using Bayesian Hierarchical Models
Henrique O. Caetano, Rahul K. Gupta, Carlos D. Maciel
In this work, we present bayesgrid, an open-source python toolbox for generating synthetic power transmission-distribution systems for any geographical location worldwide, using th…
Bayesian Model-based Generation of Synthetic Unbalanced Distribution Networks Incorporating Reliability Indices
Henrique O. Caetano, Rahul K. Gupta, Cristhian G. da R. de Oliveira +2
Real-world power distribution data are often inaccessible due to privacy and security concerns, highlighting the need for tools for generating realistic synthetic networks. Existin…
The Impact of Operators' Performance in the Reliability of Cyber-Physical Power Distribution Systems
Michel Bessani, Rodrigo Z. Fanucchi, Alexandre C. C. Delbem +1
Cyber-Physical Systems are the result of integrating information and communication technologies into physical systems. One particular case are Cyber-Physical Power Systems (CPPS),…