Edge-Computing-Based Knowledge Distillation and Multitask Learning for Partial Discharge Recognition-Reference-Cited by-同舟云学术

Edge-Computing-Based Knowledge Distillation and Multitask Learning for Partial Discharge Recognition

Published:2024 Issue: Volume:73 Page:1-11
ISSN:0018-9456
Container-title:IEEE Transactions on Instrumentation and Measurement
language:
Short-container-title:IEEE Trans. Instrum. Meas.

Author:

Ji Jinsheng¹^ORCID,Shu Zhou²^ORCID,Li Hongqun³^ORCID,Lai Kai Xian³^ORCID,Lu Minshan¹^ORCID,Jiang Guanlin¹^ORCID,Wang Wensong¹^ORCID,Zheng Yuanjin¹^ORCID,Jiang Xudong¹^ORCID

Affiliation:

1. School of Electrical and Electronic Engineering, Nanyang Technological University, Jurong West, Singapore

2. Department of Electrical and Computer Engineering, National University of Singapore, Queenstown, Singapore

3. Grid Digitalisation and Corporate Support, Singapore Power Group, Bras Basah, Singapore

Funder

Singapore Power Group and the National Research Foundation, Singapore

Energy Market Authority under its Energy Program

Nanyang Technological University

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/19/10367905/10384445.pdf?arnumber=10384445

Reference34 articles.

1. A Novel Application of Deep Belief Networks in Learning Partial Discharge Patterns for Classifying Corona, Surface, and Internal Discharges

2. Prediction of Partial Discharge Inception Voltage for Electric Vehicle Motor Insulation Using Deep Learning

3. Classification of Partial Discharge Images Using Deep Convolutional Neural Networks

4. Separation of Partial Discharges Sources and Noise Based on the Temporal and Spectral Response of the Signals

5. Classification of Partial Discharges Originating From Multilevel PWM Using Machine Learning

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