Deep Learning for Hardware-Based Real-Time Fault Detection and Localization of All Electric Ship MVDC Power System

Qin Liu, Tian Liang, Venkata Dinavahi

doi:doi:10.7939/r3-zsnj-tf70

This decommissioned ERA site remains active temporarily to support our final migration steps to https://ualberta.scholaris.ca, ERA's new home. All new collections and items, including Spring 2025 theses, are at that site. For assistance, please contact erahelp@ualberta.ca.

View

Download

Communities and Collections

Electrical and Computer Engineering, Department of / Journal Articles (Electrical and Computer Engineering)

Usage

72 views
154 downloads

Deep Learning for Hardware-Based Real-Time Fault Detection and Localization of All Electric Ship MVDC Power System

Author(s) / Creator(s)
- Qin Liu, Tian Liang, Venkata Dinavahi
Date created

2020-01-01
Subjects / Keywords
- All electric ship, correlation based feature selection, deep convolutional neural networks, fault detection and localization, field-programmable gate array, generative adversarial networks, multivariate empirical mode decomposition, mediumvoltage direct current, machine learning, random forest, real-time systems.
Type of Item

Article (Published)
DOI

https://doi.org/10.7939/r3-zsnj-tf70
License

Attribution-NonCommercial-NoDerivatives 4.0 International

Language
- English