no code implementations • 25 Oct 2022 • Brandon Foggo, Koji Yamashita, Nanpeng Yu
This paper introduces pmuGE (phasor measurement unit Generator of Events), one of the first data-driven generative model for power system event data.
1 code implementation • 3 Apr 2022 • Brandon Foggo, Koji Yamashita, Nanpeng Yu
We have trained this model on thousands of actual events and created a dataset denoted pmuBAGE (the Benchmarking Assortment of Generated PMU Events).
no code implementations • 13 Nov 2020 • Jie Shi, Brandon Foggo, Nanpeng Yu
Online power system event identification and classification is crucial to enhancing the reliability of transmission systems.
no code implementations • 10 Jun 2020 • Brandon Foggo, Nanpeng Yu
We derive the closed-form expression of the maximum mutual information - the maximum value of $I(X;Z)$ obtainable via training - for a broad family of neural network architectures.
no code implementations • 4 Nov 2019 • Brandon Foggo, Nanpeng Yu
This paper considers the problem of Phase Identification in power distribution systems.
no code implementations • 25 Feb 2019 • Brandon Foggo, Nanpeng Yu
We use this framework to prove that two methods, Facility Location Selection and Transductive Experimental Design, reduce these losses.
no code implementations • 15 Feb 2019 • Brandon Foggo, Nanpeng Yu, Jie Shi, Yuanqi Gao
It then bounds this expected total variation as a function of the size of randomly sampled datasets in a fairly general setting, and without bringing in any additional dependence on model complexity.