HEALTH ASSESSMENT OF WIND TURBINE GEARBOX BASED ON CONDITIONAL CONVOLUTION AUTOENCODING GAUSSIAN MIXTURE MODEL

被引：0

作者：

He Q. ^{[1
]}

Li Y. ^{[1
]}

Jiang G. ^{[1
]}

Su N. ^{[1
]}

Xie P. ^{[1
]}

Wu X. ^{[2
]}

机构：

[1] School of Electrical Engineering, Yanshan University, Qinhuangdao

[2] Jiangsu Guoke Intelligent Electric Co.，Ltd., Nantong

来源：

Taiyangneng Xuebao/Acta Energiae Solaris Sinica | 2023年 / 44卷 / 12期

关键词：

conditional autoencoding network; Gaussian mixture model; health assessment; SCADA; wind turbines;

D O I：

10.19912/j.0254-0096.tynxb.2022-1239

中图分类号：

学科分类号：

摘要：

To achieve health evaluation and detect wary faults of wind turbine gearboxes, a new conditional convolutional autoencoding Gaussian mixture model is proposed. The sensor information and temporal information are first encoded and decoded at the same time，and the compressed features are extracted. Then，the extracted features are input to the Gaussian mixture model to calculate the energy index based on the probability distribution as the health index for health assessment. Finally，the threshold is determined using the kernel density estimation algorithm. The effectiveness of the proposed method is verified with the supervisory control and data acquisition （SCADA）data from a real wind farm. © 2023 Science Press. All rights reserved.

引用

页码：214 / 220

页数：6

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