Fault classification and localization of multi-machine-based ieee benchmark test case power transmission lines using optimizable weighted extreme learning machine-Reference-Cited by-同舟云学术

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Fault classification and localization of multi-machine-based ieee benchmark test case power transmission lines using optimizable weighted extreme learning machine

Published:2024-10 Issue: Volume:235 Page:110857
ISSN:0378-7796
Container-title:Electric Power Systems Research
language:en
Short-container-title:Electric Power Systems Research

Author:

Hassan Mehedi,Biswas Shuvra Prokash,Chowdhury Shah Ariful Hoque^ORCID,Mondal Sudipto^ORCID,Islam Md.Rabiul,Shah Rakibuzzaman^ORCID

Publisher

Elsevier BV

Reference48 articles.

1. Methodologies in power systems fault detection and diagnosis;Aleem;Energy Syst.,2015

2. Fault classification and localization in microgrids: leveraging discrete wavelet transform and multi-machine learning techniques considering single point measurements;Basher;Electric Power Syst. Res.,2024

3. Energy efficient fault detection and classification using hyperparameter-tuned machine learning classifiers with sensors;Bhattacharya;Measurement: Sensors,2023

4. A comparative evaluation of stacked auto-encoder neural network and multi-layer extreme learning machine for detection and classification of faults in transmission lines using WAMS data;Harish;Energy and AI,2023

5. Deep learning-based application for fault location identification and type classification in active distribution grids;Rizeakos;Appl. Energy,2023

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