A novel cleaning method for wind turbine data based on QM-DBSCAN

Abstract

Abstract: For the issue of wind Speed-Power data hard cleaning in wind farms, a novel method based on QM-DBSCAN is proposed. Firstly, the wind condition which can best represent the operating state of the wind turbine is selected as the research object, and the anomalous data are classified according to the distribution characteristics. Then, the quartile method, standard DBSCAN algorithm and QM-DBSCAN method were used to identify and eliminate the abnormal data. The Spearman correlation coefficient was adopted to verify the effectiveness of the proposed method. The results indicated that the QM-DBSCAN method had the best elimination effect, which was 0.003 5 and 0.004 7 higher than the quartile method and the DBSCAN method, respectively.

Key words: wind turbine, abnormal data cleaning, the Quartile Method, DBSCAN, QM-DBSCAN

CLC Number:

TK83

ZHENG Yu-qiao, LIU Yu-han, HE Zheng-wen, Dong Bo, Wei Jian-feng. A novel cleaning method for wind turbine data based on QM-DBSCAN[J]. Journal of Lanzhou University of Technology, 2021, 47(6): 50-55.

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