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Nuclear norm-based recursive subspace identification for wind turbine flutter detections

机译：基于核规范的递归子空间识别用于风机颤振检测

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摘要

Commercial wind turbine blades are progressively becoming longer and more flexible; in order to achieve load reduction, the use of shape modifying devices is currently under research. While such modifications facilitate cost reduction, they also render the blade susceptible to the unstable aeroelastic phenomenon of flutter. To be able to detect the onset of flutter, and to modify the load control algorithm accordingly, it is desirable to perform online identification of system dynamics.In this paper, a recursive subspace identification algorithm is augmented with a nuclear norm-based cost function for the rapid identification of changes in the dominant system behavior. The time-consuming singular value thresholding step involved in the identification is replaced by a fast randomized algorithm. The method developed is used to identify the changes in the dynamics of an experimental wind turbine equipped with shape-modifying actuators, and operated under controlled conditions in a wind tunnel. The proposed identification method shows high sensitivity to changes in system dynamics, and is shown capable of stably and rapidly identifying the onset of aeroelastic flutter.
机译：商业风力涡轮机叶片正逐渐变得更长，更灵活。为了减少负荷，目前正在研究使用形状修改装置。尽管这样的修改有助于降低成本，但它们也使叶片容易受到不稳定的气动弹振现象的影响。为了能够检测到颤动的发生并相应地修改负载控制算法，需要对系统动力学进行在线识别。本文在递归子空间识别算法中增加了基于核范数的代价函数快速识别主导系统行为的变化。识别中涉及的费时的奇异值阈值化步骤由快速随机算法代替。所开发的方法用于识别配备有可变形形状的执行器并在受控条件下在风洞中运行的实验性风力涡轮机的动力学变化。所提出的识别方法对系统动力学的变化具有很高的敏感性，并且能够稳定，快速地识别出气动弹性颤动的发生。

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