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首页> 外文期刊>Mechanical systems and signal processing >Monitoring a 5 MW offshore wind energy converter-Condition parameters and triangulation based extraction of modal parameters
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Monitoring a 5 MW offshore wind energy converter-Condition parameters and triangulation based extraction of modal parameters

机译：监视5兆瓦海上风能转换器-条件参数和基于三角剖分的模态参数提取

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摘要

The test field alpha ventus is the first operating German offshore parks for wind energy. Twelve Wind Energy Converters (WECs) of the 5MW-class are installed, both, for commercial and research reasons. Due to upcoming mass production and uncertainties in loads and behaviour, monitoring the foundation of these structures was desired. Two goals addressed are the extraction of modal parameters for model validation and the estimation of condition parameters to allow a hypothesis of the system's state. In a first step the largedatabase is classified by Environmental and Operational Conditions (EOCs) through affinity propagation which is a new approach for Structural Health Monitoring (SHM) on wind turbines. Further, system identification through data driven stochastic subspace identification (SSI) is performed. A new, automated approach called triangulation-based extraction of modal parapeters (TEMP), using stability diagrams, is a key focus of the presented research. Finally, extraction of condition parameters for tower accelerations classified by EOCs, based on covariance driven SSI and Vector Auto-Regressive (VAR) Models, is performed for several observation periods from one to 16 weeks. These parameters and their distributions provide a base line for long term observations.
机译：试验场alpha ventus是德国最早运营的风能海上公园。出于商业和研究原因，均安装了12个5MW级的风能转换器（WEC）。由于即将进行的批量生产以及负载和性能的不确定性，因此需要监视这些结构的基础。解决的两个目标是用于模型验证的模态参数的提取和条件参数的估计，以允许对系统状态进行假设。第一步，大型数据库通过亲和力传播按环境和操作条件（EOC）进行分类，这是用于风力涡轮机结构健康监测（SHM）的新方法。此外，执行通过数据驱动的随机子空间标识（SSI）进行的系统标识。一种新的自动化方法，称为使用稳定性图的基于三角剖分的模态隔离物（TEMP）提取，是本研究的重点。最后，根据协方差驱动的SSI和矢量自回归（VAR）模型，对EOC分类的塔加速度条件参数进行提取，历时1至16周。这些参数及其分布为长期观察提供了基线。

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