Voltage sag source identification method based on extreme learning machine-Reference-Cited by-同舟云学术

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Voltage sag source identification method based on extreme learning machine

Published:2023-05-12 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE 6th International Electrical and Energy Conference (CIEEC)
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Author:

Junjian Chen¹,Jiekang Wu²,Zhen Lei²

Affiliation:

1. China Southern Power Grid Digital Grid Group Co, Ltd,Guangzhou,China

2. School of Automation, Guangdong University of Technology,Guangzhou,China

Funder

National Key R&D Program

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10165685/10164836/10166480.pdf?arnumber=10166480

Reference24 articles.

1. Energy demand forecasting method based on optimization deep belief network;wu;Grid Technology,2021

2. Identification method of voltage sag sources in distribution network based on wavelet entropy and probabilistic neural network;jia;Grid Technology,2009

3. Fault detection of variable rotor system based on on-line Sequential extreme learning Machine of Quantum evolution;li;Journal of Solar Energy,2022

4. Voltage Sag Disturbance Source Identification Based on GWO-SVM;luoying;Electricity and Instrumentation,2019

Cited by 1 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. Most influential feature form for supervised learning in voltage sag source localization;Engineering Applications of Artificial Intelligence;2024-07

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