Wavelet-Based Convolutional Neural Network for Denoising Partial Discharge Signals Extracted via Acoustic Emission Sensors-Reference-Cited by-同舟云学术

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Wavelet-Based Convolutional Neural Network for Denoising Partial Discharge Signals Extracted via Acoustic Emission Sensors

Published:2024-07 Issue:7 Volume:8 Page:1-4
ISSN:2475-1472
Container-title:IEEE Sensors Letters
language:
Short-container-title:IEEE Sens. Lett.

Author:

Kumar Chandan¹,Ganguly Biswarup²^ORCID,Dey Debangshu³^ORCID,Chatterjee Saibal¹^ORCID

Affiliation:

1. Department of Electrical Engineering, National Institute of Technology Mizoram, Aizawl, India

2. Department of Electrical Engineering, National Institute of Technology Silchar, Assam, India

3. Department of Electrical Engineering, Jadavpur University, Kolkata, India

Funder

Seed Money fund from NIT Mizoram

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx8/7782634/10553221/10564182.pdf?arnumber=10564182

Reference17 articles.

1. On-line Partial Discharge Localization of 10-kV Covered Conductor Lines

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

3. An Integrated Sensor With AE and UHF Methods for Partial Discharges Detection in Transformers Based on Oil Valve

4. Recurrence Plot Aided Partial Discharge Detection Framework Employing HFCT Sensor and Customized Convolutional Neural Network

5. Noise Rejection and Detection of Partial Discharges Under Repetitive Impulse Supply Voltage

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