A comprehensive research of machine learning algorithms for power quality disturbances classifier based on time-series window-Reference-Cited by-同舟云学术

A comprehensive research of machine learning algorithms for power quality disturbances classifier based on time-series window

Published:2024-01-12 Issue:4 Volume:106 Page:3983-4001
ISSN:0948-7921
Container-title:Electrical Engineering
language:en
Short-container-title:Electr Eng

Author:

Akkaya Sıtkı,Yüksek Emre,Akgün Hasan Metehan

Funder

Scientific Research Unity of Sivas University of Science and Technology.

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s00202-023-02177-8.pdf

Reference43 articles.

1. IEEE Power and Energy Society (2009) In: IEEE—Recommended practice for monitoring electric power quality, vol 1995, no 1

2. IEEE Power & Energy Society (2010) In: 2010–1459-IEEE standard definitions for the measurement of electric power quantities under sinusoidal, nonsinusoidal, balanced, or unbalanced conditions

3. I. Standard and N. Internationale (2008) IEC Standard 61000-4-7: general guide on harmonics and interharmonics measurements and measuring instruments for power supply networks and attached devices used for the measurements

4. I. Power and E. Society (2011) IEEE recommended practice-adoption of IEC 61000-4-15: 2010, electromagnetic compatibility (EMC)-testing and measurement techniques-Flickermeter-functional and design specifications, no. October

5. International Electrotechnical Commission (2003) IEC Standard 61000-4-30: testing and measurement techniques-power quality measurement methods, vol 2003, p 90

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