A Novel Deep-Learning Framework to Identify and Locate Single and Multiple Partial Discharge Events-Reference-Cited by-同舟云学术

A Novel Deep-Learning Framework to Identify and Locate Single and Multiple Partial Discharge Events

Published:2023-12 Issue:6 Volume:30 Page:2633-2641
ISSN:1070-9878
Container-title:IEEE Transactions on Dielectrics and Electrical Insulation
language:
Short-container-title:IEEE Trans. Dielect. Electr. Insul.

Author:

Das Rakesh¹,Das Arup K.²^ORCID,Chatterjee Soumya³^ORCID,Pradhan Arpan K.¹^ORCID,Biswas Subrata⁴,Dalai Sovan¹^ORCID,Chatterjee Biswendu¹^ORCID,Bhattacharya Kesab¹

Affiliation:

1. Electrical Engineering Department, Jadavpur University, Kolkata, India

2. FAMU-FSU College of Engineering, Tallahassee, FL, USA

3. Electrical Engineering Department, NIT Durgapur, Durgapur, West Bengal, India

4. Department of Electrical Engineering, Netaji Subhash Engineering College, Kolkata, India

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/94/10375119/10163404.pdf?arnumber=10163404

Reference18 articles.

1. Recent Trends in the Condition Monitoring of Transformers

2. Partial Discharge Detection Framework Employing Spectral Analysis of Horizontal Visibility Graph

3. Wavelet Kernel based Convolutional Neural Network for Localization of Partial Discharge Sources within a Power Apparatus

4. A methodology for identification and localization of partial discharge sources using optical sensors

5. An approach based on rough set theory for identification of single and multiple partial discharge source

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