Fault classification in power distribution systems based on limited labeled data using multi-task latent structure learning-Reference-Cited by-同舟云学术

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Fault classification in power distribution systems based on limited labeled data using multi-task latent structure learning

Published:2021-10 Issue: Volume:73 Page:103094
ISSN:2210-6707
Container-title:Sustainable Cities and Society
language:en
Short-container-title:Sustainable Cities and Society

Author:

Gilanifar Mostafa,Wang Hui,Cordova Jose,Ozguven Eren Erman,Strasser Thomas I.,Arghandeh Reza

Funder

National Science Foundation

Horizon 2020 European Innovation Council Fast Track to Innovation

Publisher

Elsevier BV

Subject

Transportation,Renewable Energy, Sustainability and the Environment,Civil and Structural Engineering,Geography, Planning and Development

Reference27 articles.

1. A review on machine learning forecasting growth trends and their real-time applications in different energy systems;Ahmad;Sustainable Cities and Society,2020

2. Integrated use of time-frequency wavelet decompositions for fault location in distribution networks: Theory and experimental validation;Borghetti;IEEE Transactions on Power Delivery,2010

3. Disturbance ratio for optimal multi-event classification in power distribution networks;Borrás;IEEE Transactions on Industrial Electronics,2016

4. A continuation method for semi-supervised SVMs;Chapelle,2006

5. Novel approach for optimum coordination of overcurrent relays to enhance microgrid earth fault protection scheme;El-Naily;Sustainable Cities and Society,2020

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4. Power transmission system’s fault location, detection, and classification: Pay close attention to transmission nodes;International Journal of Electrical Power & Energy Systems;2024-02

5. Nonlinear Data Classification of Power Internet of Things Considering Transient and Steady State;2023 3rd International Conference on Electrical Engineering and Control Science (IC2ECS);2023-12-29

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