[1]YIN Shi,HOU Guolian,HU Xiaodong,et al.Temperature monitoring method of the main bearing of wind turbine based on AC-GAN data reconstruction[J].CAAI Transactions on Intelligent Systems,2021,16(6):1106-1116.[doi:10.11992/tis.202009020]
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CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume:
16
Number of periods:
2021 6
Page number:
1106-1116
Column:
学术论文—知识工程
Public date:
2021-11-05
- Title:
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Temperature monitoring method of the main bearing of wind turbine based on AC-GAN data reconstruction
- Author(s):
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YIN Shi1; 2; HOU Guolian2; HU Xiaodong2; ZHOU Jiwei1
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1. ZhongNeng Power-Tech Development Co., LTD, Beijing 100034, China;
2. College of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China
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- Keywords:
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light gradient boosting machine; auxiliary classifier generative adversarial networks; natural gradient boosting; wind turbines; main bearing; condition monitoring; data reconstruction; temperature residual
- CLC:
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TP8;TK83
- DOI:
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10.11992/tis.202009020
- Abstract:
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To better identify the operating status of the main bearing of wind turbines, a data reconstruction algorithm based on auxiliary classifier generative adversarial networks (AC-GAN) is proposed to monitor the temperature of the main bearing of the wind turbine. First, this work uses the time series data of the supervisory control and data acquisition system to establish the wind turbine’s main bearing temperature prediction model based on the light gradient boosting machine and calculates its residual characteristics. Second, the statistical process control (SPC) method is used to screen abnormal temperature residuals of the main bearing within the control line, and the AC-GAN algorithm is used to reconstruct the residual sequence. Finally, normal and abnormal temperature residual characteristics of the main bearing are extracted, and the main bearing status monitoring model based on the natural gradient boosting algorithm is established. Experimental results show that the accuracy of the method for judging the operating state of the main bearing is as high as 87.5%, for which the algorithm can effectively monitor the running state of wind turbine bearings.