Partial Discharge Data Augmentation Based on Improved Wasserstein Generative Adversarial Network With Gradient Penalty
Author:
Affiliation:
1. High Voltage Laboratory in the College of Electrical Engineering, Sichuan University, Chengdu, China
2. College of Electronics and Information Engineering, Sichuan University, Chengdu, China
Funder
National Natural Science Foundation of China
Natural Science Foundation of Sichuan Province
Fundamental Research Funds for the Central Universities
Science Foundation of Sichuan Science and Technology Department
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering,Computer Science Applications,Information Systems,Control and Systems Engineering
Link
http://xplorestaging.ieee.org/ielx7/9424/10116046/09854145.pdf?arnumber=9854145
Reference44 articles.
1. A Convolutional Neural Network-Based Deep Learning Methodology for Recognition of Partial Discharge Patterns from High-Voltage Cables
2. Data augmentation method for power transformer fault diagnosis based on conditional Wasserstein generative adversarial network;liu;Power System Technol,2020
3. A Novel Application of Deep Belief Networks in Learning Partial Discharge Patterns for Classifying Corona, Surface, and Internal Discharges
4. Artificial Generation of Partial Discharge Sources Through an Algorithm Based on Deep Convolutional Generative Adversarial Networks
5. Resampling-Based Ensemble Methods for Online Class Imbalance Learning
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