[1]尹诗,侯国莲,胡晓东,等.基于AC-GAN数据重构的风电机组主轴承温度监测方法[J].智能系统学报,2021,16(6):1106-1116.[doi:10.11992/tis.202009020]
 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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基于AC-GAN数据重构的风电机组主轴承温度监测方法(/HTML)
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第16卷
期数:
2021年6期
页码:
1106-1116
栏目:
学术论文—知识工程
出版日期:
2021-11-05

文章信息/Info

Title:
Temperature monitoring method of the main bearing of wind turbine based on AC-GAN data reconstruction
作者:
尹诗12 侯国莲2 胡晓东2 周继威1
1. 中能电力科技开发有限公司,北京 100034;
2. 华北电力大学 控制与计算机工程学院,北京 102206
Author(s):
YIN Shi12 HOU Guolian2 HU Xiaodong2 ZHOU Jiwei1
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
关键词:
轻型梯度增强学习器辅助分类生成对抗网络自然梯度提升风电机组主轴承状态监测数据重构温度残差
Keywords:
light gradient boosting machineauxiliary classifier generative adversarial networksnatural gradient boostingwind turbinesmain bearingcondition monitoringdata reconstructiontemperature residual
分类号:
TP8;TK83
DOI:
10.11992/tis.202009020
摘要:
为更好地识别风电机组主轴承运行状态,提出了一种基于辅助分类生成对抗网络(auxiliary classifier generative adversarial networks, AC-GAN)的数据重构算法对风电机组主轴承温度进行监测。首先,利用采集与监视控制系统(supervisory control and data acquisition, SCADA)时序数据建立基于轻型梯度增强学习器(light gradient boosting machine, LightGBM)的主轴承温度预测模型,并计算其残差特征。其次,利用统计过程控制(statistical process control, SPC)方法对主轴承温度异常残差在控制线范围内进行筛选,并利用AC-GAN算法对残差进行重构。最后,分别提取主轴承温度正常和异常的残差特征,建立基于自然梯度提升(natural gradient boosting, NGBoost)的主轴承状态监测模型。实验结果表明,该方法对主轴承运行状态判断准确度高达87.5%,能够有效地监测风电机组轴承类运行状态。
Abstract:
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.

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备注/Memo

备注/Memo:
收稿日期:2020-09-15。
基金项目:国家自然科学基金项目(61973116)
作者简介:尹诗,博士研究生,主要研究方向为风电机组故障预警、新能源状态监测;侯国莲,教授,博士生导师,主要研究方向为风电故障诊断、复杂系统优化控制、新能源技术及控制策略;胡晓东,硕士研究生,主要研究方向为风电机组故障诊断和预警
通讯作者:尹诗.E-mail:yinshi502@163.com
更新日期/Last Update: 2021-12-25