Machine Learning with Adaptive Rate Processing for Power Quality Disturbances Identification-Reference-Cited by-同舟云学术

Machine Learning with Adaptive Rate Processing for Power Quality Disturbances Identification

Published:2021-10-26 Issue:1 Volume:3 Page:
ISSN:2662-995X
Container-title:SN Computer Science
language:en
Short-container-title:SN COMPUT. SCI.

Author:

Mian Qaisar Saeed^ORCID,Alyamani Nehal,Waqar Asad,Krichen Moez

Funder

effat university

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s42979-021-00904-1.pdf

Reference28 articles.

1. Van den Broeck G, Stuyts J, Driesen J. A critical review of power quality standards and definitions applied to DC microgrids. Appl Energy. 2018;229:281–8.

2. Kumar V, Pandey AS, Sinha SK. Grid integration and power quality issues of wind and solar energy system: a review. Int Conf Emerg Trends Electr Electron Sustain Energy Syst (ICETEESES). 2016;2016:71–80.

3. Rönnberg S, Bollen M. Power quality issues in the electric power system of the future. Electr J. 2016;29(10):49–61.

4. Ali ND, Zakri AA. Identifying characteristic of power quality problems on solar electric power generation. Int J Electr Energy Power Syst Eng. 2018;1(2):7–10.

5. Latran MB, Teke A, Yoldaş Y. Mitigation of power quality problems using distribution static synchronous compensator: a comprehensive review. IET Power Electron. 2015;8(7):1312–28.

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