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Found 7 papers, 1 papers with code
pmuBAGE: The Benchmarking Assortment of Generated PMU Data for Power System Events
This paper introduces pmuGE (phasor measurement unit Generator of Events), one of the first data-driven generative model for power system event data.
pmuBAGE: The Benchmarking Assortment of Generated PMU Data for Power System Events -- Part I: Overview and Results
We have trained this model on thousands of actual events and created a dataset denoted pmuBAGE (the Benchmarking Assortment of Generated PMU Events).
Power System Event Identification based on Deep Neural Network with Information Loading
Online power system event identification and classification is crucial to enhancing the reliability of transmission systems.
On the Maximum Mutual Information Capacity of Neural Architectures
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.
Improving Supervised Phase Identification Through the Theory of Information Losses
This paper considers the problem of Phase Identification in power distribution systems.
Analyzing Data Selection Techniques with Tools from the Theory of Information Losses
We use this framework to prove that two methods, Facility Location Selection and Transductive Experimental Design, reduce these losses.
Information Losses in Neural Classifiers from Sampling
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.
