9 citations · 16 across the 8 of their papers we have counts for
6 papers · 1 filter
pmuBAGE: The Benchmarking Assortment of Generated PMU Data for Power System Events -- Part I: Overview and Results
Brandon Foggo, Koji Yamashita, Nanpeng Yu
We present pmuGE (phasor measurement unit Generator of Events), one of the first data-driven generative model for power system event data. We have trained this model on thousands o…
Machine Learning-Driven Virtual Bidding with Electricity Market Efficiency Analysis
Yinglun Li, Nanpeng Yu, Wei Wang
This paper develops a machine learning-driven portfolio optimization framework for virtual bidding in electricity markets considering both risk constraint and price sensitivity. Th…
Estimate Three-Phase Distribution Line Parameters With Physics-Informed Graphical Learning Method
Wenyu Wang, Nanpeng Yu
Accurate estimates of network parameters are essential for modeling, monitoring, and control in power distribution systems. In this paper, we develop a physics-informed graphical l…
Power System Event Identification based on Deep Neural Network with Information Loading
Jie Shi, Brandon Foggo, Nanpeng Yu
Online power system event identification and classification is crucial to enhancing the reliability of transmission systems. In this paper, we develop a deep neural network (DNN) b…
Improving Supervised Phase Identification Through the Theory of Information Losses
Brandon Foggo, Nanpeng Yu
This paper considers the problem of Phase Identification in power distribution systems. In particular, it focuses on improving supervised learning accuracies by focusing on exploit…
Information Losses in Neural Classifiers from Sampling
Brandon Foggo, Nanpeng Yu, Jie Shi +1
This paper considers the subject of information losses arising from the finite datasets used in the training of neural classifiers. It proves a relationship between such losses as…