Prediction and classification of voltage sag trend based on support vector machine with parameter optimization-Reference-Cited by-同舟云学术

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Prediction and classification of voltage sag trend based on support vector machine with parameter optimization

Published:2023-05-12 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE 6th International Electrical and Energy Conference (CIEEC)
language:
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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/ielx7/10165685/10164836/10166514.pdf?arnumber=10166514

Reference26 articles.

1. Application of fuzzy comprehensive evaluation in source identification of voltage sag;li;Grid Technologies,2017

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

3. Voltage sag disturbance source identification based on GWO-SVM;zhao;Electricity and Instrumentation,2019

4. Voltage sag source identification method based on optimized extreme learning machine;wang;Automation of Electric Power Systems,2020

5. Identification of voltage sag source in distribution network based on BAS-SVM;liu;China Power,2022

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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