Monitoring Wind Turbine Vibration Based on SCADA Data
Monitoring Wind Turbine Vibration Based on SCADA Data
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DOI：
10.1115/1.4005753
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发表时间：
2012-05
影响因子：
2.3
通讯作者：
Zijun Zhang;A. Kusiak
中科院分区：
文献类型：
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作者：
Zijun Zhang;A. Kusiak
讨论了三种反映在时域上的涡轮机振动异常检测模型。这些模型来自于在各种风力涡轮机上收集的监控和数据采集（SCADA）数据。风力涡轮机的振动由两个参数表征，即，动力传动系统和塔加速。一个无监督的数据挖掘算法，k-means聚类算法，应用于开发第一个监测模型。利用控制图的概念建立了动力传动系统和塔架加速度异常值的监测模型。以10 s间隔采样的SCADA振动数据反映了风机的正常和故障状态。利用SCADA工业数据验证了3种监测模型在检测时域振动数据中反映的风机异常时的性能。
Three models for detecting abnormalities of wind turbine vibrations reflected in time domain are discussed. The models were derived from the supervisory control and data acquisition (SCADA) data collected at various wind turbines. The vibration of a wind turbine is characterized by two parameters, i.e., drivetrain and tower acceleration. An unsupervised data-mining algorithm, the k-means clustering algorithm, was applied to develop the first monitoring model. The other two monitoring models for detecting abnormal values of drivetrain and tower acceleration were developed by using the concept of a control chart. SCADA vibration data sampled at 10 s intervals reflects normal and faulty status of wind turbines. The performance of the three monitoring models for detecting abnormalities of wind turbines reflected in vibration data of time domain was validated with the SCADA industrial data.