Abnormal power data cleaning method of the wind turbine based on improved DBSCAN

Abstract

Abstract: The anomalous power data of wind turbine are difficult to clean effectively, an improved DBSCAN method was proposed. Firstly, the data set was segmented discretely. Then, the parameters of DBSCNA algorithm were estimated adaptively and clustered in each discrete interval. Ultimately, the statistical feature similarity was calculated to correct the clustering results. The proposed method is validated with the measured data of the wind turbine SCADA system in a wind field of 2.5 MW. The findings indicate that the recall rate, accuracy rate and F1 value of the improved method are 97.97%, 97.97% and 97.85%, respectively. The improved method can effectively clean the wind turbine power data set, and the improved method is more stable when the data set is changed.

Key words: wind turbine, abnormal power, data cleaning, cleaning quality, improved DBSCAN

CLC Number:

TK83

LI Lin, DONG Bo, ZHENG Yu-qiao. Abnormal power data cleaning method of the wind turbine based on improved DBSCAN[J]. Journal of Lanzhou University of Technology, 2022, 48(3): 65-70.

References

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