子空间拓扑
计算机科学
涡轮机
塔楼
结构健康监测
风力发电
鉴定(生物学)
振动
状态监测
风速
海洋工程
结构工程
声学
人工智能
工程类
航空航天工程
气象学
物理
电气工程
生物
植物
作者
Kaoshan Dai,Ying Wang,Wensheng Liu,Xiaobing Ren,Zhenhua Huang
摘要
Structural health monitoring (SHM) of wind turbines has been applied in the wind energy industry to obtain their real-time vibration parameters and to ensure their optimum performance. For SHM, the accuracy of its results and the efficiency of its measurement methodology and data processing algorithm are the two major concerns. Selection of proper measurement parameters could improve such accuracy and efficiency. The Stochastic Subspace Identification (SSI) is a widely used data processing algorithm for SHM. This research discussed the accuracy and efficiency of SHM using SSI method to identify vibration parameters of on-line wind turbine towers. Proper measurement parameters, such as optimum measurement duration, are recommended.
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