• 中国计算机学会会刊
  • 中国科技核心期刊
  • 中文核心期刊

Computer Engineering & Science

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Health assessment of wind turbine generator
based on improved stacked auto-encoder
 

LIN Tao,ZHAO Cheng-lin,LIU Hang-peng,ZHAO Shen-shen   

  1. (School of Artificial Intelligence,Hebei University of Technology,Tianjin 300130,China)
  • Received:2019-06-10 Revised:2019-10-11 Online:2020-03-25 Published:2020-03-25

Abstract:

Wind turbine generator has the characteristics of complicated structure and difficult maintenance. In order to evaluate its health, this paper combines the characteristics of denoising auto-encoder and sparse auto-encoder to improve the traditional stacked auto-encoder model, and uses the reconstruction error of the model to monitor the running state of the wind turbine generator. The reconstruction error obtained by off-line testing is compared with the reconstruction error obtained by online monitoring, and the health of the wind turbine generator is obtained by combining three different indicators. The health assessment model is trained and tested by using the actual data of a wind farm in Hebei Pro- vince. The example analysis shows that the method can effectively track the state change of the wind turbine generator and has the function of early identification of faults.

 

 

 

 

 

Key words: wind turbine generator, health assessment, stacked auto-encoder, denoising auto-encoder, sparse auto-encoder