Proceedings of the 3rd International Conference on Wireless Communication and Sensor Networks (WCSN 2016)

Big Data-based Harmonic Problem Research in Wind Farms

Authors
Song-Tao Yu, Da Xie, Yu-Pu Lu, Zu-Yi Zhao, Yan-Chi Zhang
Corresponding Author
Song-Tao Yu
Available Online December 2016.
DOI
https://doi.org/10.2991/icwcsn-16.2017.105How to use a DOI?
Keywords
Keywords-Big Data; Wind Farms; Harmonics; Data Mining; Clustering.
Abstract
More and more attention on the power quality of wind farms has been paid recent years, in which harmonic problem is one of the most concerned. On the other hand, power big data in wind farm generated all the time, and the volume is increasing continuously, which can be mined to extract some special or new information to solve current operating problem and adjust the running conditions of wind turbines. In this paper, a novel algorithm for power big data analysis has been put forward by a combined application of conventional harmonic analyzing method and typical clustering algorithm, which can be used to deal with the big data in wind farm to study harmonic problem. The measured big data of a 2MW DFIG wind turbine in operation have been used to verify the new algorithm, and some interesting conclusions of harmonics have been found at last.
Open Access
This is an open access article distributed under the CC BY-NC license.

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Proceedings
3rd International Conference on Wireless Communication and Sensor Networks (WCSN 2016)
Part of series
Advances in Computer Science Research
Publication Date
December 2016
ISBN
978-94-6252-302-9
ISSN
2352-538X
DOI
https://doi.org/10.2991/icwcsn-16.2017.105How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Song-Tao Yu
AU  - Da Xie
AU  - Yu-Pu Lu
AU  - Zu-Yi Zhao
AU  - Yan-Chi Zhang
PY  - 2016/12
DA  - 2016/12
TI  - Big Data-based Harmonic Problem Research in Wind Farms
BT  - 3rd International Conference on Wireless Communication and Sensor Networks (WCSN 2016)
PB  - Atlantis Press
SN  - 2352-538X
UR  - https://doi.org/10.2991/icwcsn-16.2017.105
DO  - https://doi.org/10.2991/icwcsn-16.2017.105
ID  - Yu2016/12
ER  -