1 citations · 1 across the 10 of their papers we have counts for
5 papers · 1 filter
Improving accuracy and convergence of federated learning edge computing methods for generalized DER forecasting applications in power grid
Vineet Jagadeesan Nair, Lucas Pereira
This proposal aims to develop more accurate federated learning (FL) methods with faster convergence properties and lower communication requirements, specifically for forecasting di…
Enhanced physics-informed neural networks (PINNs) for high-order power grid dynamics
Vineet Jagadeesan Nair
We develop improved physics-informed neural networks (PINNs) for high-order and high-dimensional power system models described by nonlinear ordinary differential equations. We prop…
Federated Learning Forecasting for Strengthening Grid Reliability and Enabling Markets for Resilience
Lucas Pereira, Vineet Jagadeesan Nair, Bruno Dias +2
We propose a comprehensive approach to increase the reliability and resilience of future power grids rich in distributed energy resources. Our distributed scheme combines federated…
Resilience of the Electric Grid through Trustable IoT-Coordinated Assets (Extended version)
Vineet J. Nair, Venkatesh Venkataramanan, Priyank Srivastava +10
The electricity grid has evolved from a physical system to a cyber-physical system with digital devices that perform measurement, control, communication, computation, and actuation…
A game-theoretic, market-based approach to extract flexibility from distributed energy resources
Vineet Jagadeesan Nair, Anuradha Annaswamy
We propose a market designed using game theory to optimally utilize the flexibility of distributed energy resources (DERs) like solar, batteries, electric vehicles, and flexible lo…