Fault Detection and Classification in Ring Power System With DG Penetration Using Hybrid CNN-LSTM-Reference-Cited by-同舟云学术

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Fault Detection and Classification in Ring Power System With DG Penetration Using Hybrid CNN-LSTM

Published:2024 Issue: Volume:12 Page:59953-59975
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Alhanaf Ahmed Sami¹^ORCID,Farsadi Murtaza²^ORCID,Balik Hasan Hüseyin³

Affiliation:

1. Department of Computer Engineering, Yildiz Technical University (YTU), Istanbul, Turkey

2. Department of Electrical Engineering, Faculty of Engineering, Istanbul Aydın University, Istanbul, Turkey

3. Department of Computer Engineering, Faculty of Engineering, Istanbul Aydın University, Istanbul, Turkey

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/6287639/10380310/10508728.pdf?arnumber=10508728

Reference60 articles.

1. Fault‐cause identification method based on adaptive deep belief network and time–frequency characteristics of travelling wave

2. Frequency Disturbance Event Detection Based on Synchrophasors and Deep Learning

3. Transmission line fault detection and classification based on SA-MobileNetV3

4. Fault detection of wind turbine based on SCADA data analysis using CNN and LSTM with attention mechanism

5. Transmission Line Fault Classification Based on Dynamic State Estimation and Support Vector Machine

Cited by 2 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. Sensors Faults Classification and Faulty Signals Reconstruction Using Deep Learning;IEEE Access;2024

2. A Novel Ensemble Learning-Based Method for Online Total Transfer Capability Assessment of New Power System With Increased Stochastics;IEEE Access;2024

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