Deep Learning Based Relay for Online Fault Detection, Classification, and Fault Location in a Grid-Connected Microgrid-Reference-Cited by-同舟云学术

Deep Learning Based Relay for Online Fault Detection, Classification, and Fault Location in a Grid-Connected Microgrid

Published:2023 Issue: Volume:11 Page:62674-62696
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Roy Bappa¹^ORCID,Adhikari Shuma¹,Datta Subir²^ORCID,Devi K. Jilenkumari¹,Devi Aribam Deleena³,Alsaif Faisal⁴^ORCID,Alsulamy Sager⁵,Ustun Taha Selim⁶^ORCID

Affiliation:

1. Electrical Engineering Department, National Institute of Technology Manipur, Imphal, Manipur, India

2. Department of Electrical Engineering, Mizoram University, Aizawl, Mizoram, India

3. Electrical Engineering Department, National Institute of Technology Silchar, Silchar, Assam, India

4. Department of Electrical Engineering, College of Engineering, King Saud University, Riyadh, Saudi Arabia

5. Energy and Climate Change Division, Sustainable Energy Research Group, Faculty of Engineering and Physical Sciences, University of Southampton, Southampton, U.K

6. Fukushima Renewable Energy Institute (FREA), National Institute of Advanced Industrial Science and Technology (AIST), Koriyama, Japan

Funder

Researchers Supporting Project number

King Saud University, Riyadh, Saudi Arabia

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/10005208/10149318.pdf?arnumber=10149318

Reference40 articles.

1. A Novel Method for Fault Detection in Future Renewable Electric Energy Delivery and Management Microgrids, Considering Uncertainties in Network Topology

2. LSTM-based Quick Event Detection in Power Systems

3. Artificial Intelligence Based Intrusion Detection System for IEC 61850 Sampled Values Under Symmetric and Asymmetric Faults

4. Power System Load Forecasting Method Based on LSTM Network

5. A review on fault classification methodologies in power transmission systems: Part—I

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