Partial Discharge Pattern Recognition Based on a Multifrequency F–P Sensing Array, AOK Time–Frequency Representation, and Deep Learning
Author:
Affiliation:
1. State Key Laboratory of Power Transmission Equipment & System Security and New Technology, Chongqing University, Chongqing, China
Funder
Science and Technology Project of State Grid Corporation of China
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering
Link
http://xplorestaging.ieee.org/ielx7/94/9903995/09858898.pdf?arnumber=9858898
Reference27 articles.
1. Response Bandwidth Design of Fabry-Perot Sensors for Partial Discharge Detection Based on Frequency Analysis
2. Condition Monitoring Based on Partial Discharge Diagnostics Using Machine Learning Methods: A Comprehensive State-of-the-Art Review
3. Application possibilities of artificial neural networks for recognizing partial discharges measured by the acoustic emission method
4. Identification of multiple partial discharge sources using acoustic emission technique and blind source separation
5. Application of improved Hilbert-Huang transform to partial discharge signal analysis
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