Clustering by communication with local agents for noise and multiple partial Discharges discrimination-Reference-Cited by-同舟云学术

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Clustering by communication with local agents for noise and multiple partial Discharges discrimination

Published:2023-09 Issue: Volume:225 Page:120067
ISSN:0957-4174
Container-title:Expert Systems with Applications
language:en
Short-container-title:Expert Systems with Applications

Author:

Boya-Lara Carlos,Rivera-Caballero Omar^ORCID,Alfredo Ardila-Rey Jorge

Funder

Universidad Técnica Federico Santa María

Fondo Nacional de Desarrollo Científico y Tecnológico

Secretaría Nacional de Ciencia, Tecnología e Innovación

Fondo de Fomento al Desarrollo Científico y Tecnológico

Sistema Nacional de Investigadores

Agencia Nacional de Investigación y Desarrollo

Publisher

Elsevier BV

Subject

Artificial Intelligence,Computer Science Applications,General Engineering

Reference53 articles.

1. Analysis of ultrawide-band detected partial discharges by means of a multiresolution digital signal-processing method;Angrisani;Measurement: Journal of the International Measurement Confederation,2000

2. Separation techniques of partial discharges and electrical noise sources: A review of recent progress;Ardila-Rey;IEEE Access,2020

3. Automatic selection of frequency bands for the power ratios separation technique in partial discharge measurements: Part II, PD source recognition and applications;Ardila-Rey;IEEE Transactions on Dielectrics and Electrical Insulation,2015

4. Partial discharge and noise separation by means of spectral-power clustering techniques;Ardila-Rey;IEEE Transactions on Dielectrics and Electrical Insulation,2013

5. Separation of partial discharges sources and noise based on the temporal and spectral response of the signals;Ardila-Rey;IEEE Transactions on Instrumentation and Measurement,2021

Cited by 4 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Partial Discharge Pattern Recognition in High Voltage Transmission Cable Project;2024 IEEE 4th International Conference on Electronic Technology, Communication and Information (ICETCI);2024-05-24

2. Noise Separation and Discriminative Feature Learning for Partial Discharge Recognition;IEEE Transactions on Industrial Informatics;2024

3. Unsupervised Deep Learning for Detecting Number of Partial Discharge Sources in Stator Bars;IEEE Transactions on Dielectrics and Electrical Insulation;2023-12

4. Denoising of partial discharge signal using a hybrid framework of total variation denoising-autoencoder;Measurement;2023-12

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