An imbalanced deep learning framework for pre-fault flexible multi-zone dynamic security assessment via transfer learning based graph convolutional network
Deep learning (DL)-based pre-fault dynamic security assessment (DSA) methods have shown promising results. However, DL-based DSA faces challenges related to model robustness against topology changes and database imbalances. Although an accurate model can be trained for a particular topology, it ofte...
Saved in:
Main Authors: | Sasan Azad, Mohammad Taghi Ameli |
---|---|
Format: | Article |
Language: | English |
Published: |
Elsevier
2025-03-01
|
Series: | Results in Engineering |
Subjects: | |
Online Access: | http://www.sciencedirect.com/science/article/pii/S2590123025002609 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
MCRCNet: A Bearing Fault Diagnosis Method for Unknown Faults Based on Transfer Learning
by: Guangyuan Xu, et al.
Published: (2025-01-01) -
Rolling Bearing Fault Diagnosis Based on a Synchrosqueezing Wavelet Transform and a Transfer Residual Convolutional Neural Network
by: Zihao Zhai, et al.
Published: (2025-01-01) -
A Novel Convolutional Neural Network-Based Approach for Fault Classification in Photovoltaic Arrays
by: Farkhanda Aziz, et al.
Published: (2020-01-01) -
A REAL TIME FACE RECOGNITION SYSTEM USING ALEXNET DEEP CONVOLUTIONAL NETWORK TRANSFER LEARNING MODEL
by: LAWRENCE O. OMOTOSHO, et al.
Published: (2021-10-01) -
Towards a Standard Benchmarking Framework for Domain Adaptation in Intelligent Fault Diagnosis
by: Mohammed M. Farag
Published: (2025-01-01)