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Early warning and diagnosis of primary fans based on improved MSET

Published:2023-06-25 author:YU Xing-gang, BIN Yi-yuan, CHEN Wen,et al. Browse: 408 Check PDF documents

Early warning and diagnosis of primary fans based on improved MSET

YU Xing-gang1, BIN Yi-yuan2, CHEN Wen2, WEI Xin3, LIU Ming3, QIU Bin-bin3

(1.Hunan Province Key Laboratory of Efficient & Clean Power Generation Technologies, State Grid Hunan Electric

Power Corporation Limited Research Institute, Changsha 410007, China; 2.Hunan Xiangdian Test & Research

Institute Co.,Ltd., Changsha 410004, China; 3.State Key Laboratory of Multiphase Flow in Power Engineering,

Xi‘’an Jiaotong University, Xi‘’an 710049, China)

Abstract: Aiming at the problems of wind turbine fault early warning and fault point tracing in power station, a wind turbine fault early warning method based on improved multivariate state estimation technique (MSET)and error component was proposed. Firstly, the multivariate state estimation technology (MSET)was introduced. The modeling variables were selected, data preprocessing was performed, and the validity of model validity was verified by constructing an improved dynamic memory matrix D. Then, the similarity function was used as the basis for fault early warning, the sliding window method was used to reduce noise interference, and two kinds of dynamic memory matrix construction methods were used to build models and verify their effectiveness. Finally, the temperature and vibration fault data were simulated by artificially increasing the disturbance to carry out fault warning simulation, and the fault point was traced by calculating the error components of each parameter. The results show that the improved dynamic memory matrix modeling method has higher accuracy and stronger antiinterference ability. The improved MSET and error component model can successfully realize the early warning of faults and the traceability of fault points. This model can provide effective guidance for early warning and maintenance of power station equipment failures.

Key words: centrifugal blower; multivariate state estimation technique(MSET); error component; fault point tracking; dynamic memory matrix modeling method; artificially increasing the disturbance; similarity function

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