Localisation of Partial Discharge in Power Cables Through Multi-Output Convolutional Recurrent Neural Network and Feature Extraction

Author:

Yeo Joel1ORCID,Jin Huifei2,Mor Armando Rodrigo3,Yuen Chau1ORCID,Pattanadech Norasage4,Tushar Wayes5ORCID,Saha Tapan K.5ORCID,Ng Chee Seng2

Affiliation:

1. Engineering Product Development pillar, Singapore University of Technology and Design, Singapore

2. T Yeo Engineers Pte. Ltd., Singapore

3. Universitat Politècnica de València, Instituto de Tecnología Eléctrica, Valencia, Spain

4. Faculty of Engineering, Electrical Engineering Department, King Mongkut's Institute of Technology Ladkrabang, Bangkok, Thailand

5. School of Information Technology and Electrical Engineering, University of Queensland, Queensland, Australia

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Energy Engineering and Power Technology

Cited by 8 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. Identification of emitting times based on improved CRNN model;Journal of Physics: Conference Series;2024-08-01

2. Deep learning approach for cable partial discharge pattern identification;Electrical Engineering;2024-07-16

3. Pre-Breakdown Condition of Insulation Recognition by Partial Discharges Characteristics;2024 International Conference on Industrial Engineering, Applications and Manufacturing (ICIEAM);2024-05-20

4. Machine Learning Applications for Online Partial Discharge Detection, Classification, and Localization in Power Transformers: A Review;2024 4th International Conference on Smart Grid and Renewable Energy (SGRE);2024-01-08

5. Diagnosis Method for Partial Discharge Faults in Power Cables Based on CEEMDAN-IZOA-DBN;2023 7th International Conference on Power and Energy Engineering (ICPEE);2023-12-22

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