Proceedings of the 2nd International Conference on Electrical and Electronic Engineering (EEE 2019)

Operational State Analysis of Wind Turbines Based on SCADA Data

Authors
Jing-chun Chu, Ling Yuan, Fa Xie, Lei Pan, Xiao-dan Wang, Lin-zhong Zhang
Corresponding Author
Fa Xie
Available Online July 2019.
DOI
10.2991/eee-19.2019.29How to use a DOI?
Keywords
Wind turbines, State evaluation, PCA method, SCADA data
Abstract

For the multi-parameters evaluation problem of wind turbine operational status, the principal component analysis (PCA) methodology is firstly used to reduce dimension, standardize and de-correlate to the SCADA data of wind turbine operation, and get the principal component of the wind turbine operational SCADA data. Secondly, computing the total score of the principal component, which is utilized for evaluating the wind turbine. Finally, the operational status of nine wind turbines in a same wind farm are compared and analyzed by using PCA method proposed in this paper. The results show that the PCA method is effective for evaluating wind turbines.

Copyright
© 2019, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 2nd International Conference on Electrical and Electronic Engineering (EEE 2019)
Series
Advances in Engineering Research
Publication Date
July 2019
ISBN
10.2991/eee-19.2019.29
ISSN
2352-5401
DOI
10.2991/eee-19.2019.29How to use a DOI?
Copyright
© 2019, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Jing-chun Chu
AU  - Ling Yuan
AU  - Fa Xie
AU  - Lei Pan
AU  - Xiao-dan Wang
AU  - Lin-zhong Zhang
PY  - 2019/07
DA  - 2019/07
TI  - Operational State Analysis of Wind Turbines Based on SCADA Data
BT  - Proceedings of the 2nd International Conference on Electrical and Electronic Engineering (EEE 2019)
PB  - Atlantis Press
SP  - 169
EP  - 173
SN  - 2352-5401
UR  - https://doi.org/10.2991/eee-19.2019.29
DO  - 10.2991/eee-19.2019.29
ID  - Chu2019/07
ER  -