基于高斯过程潜在力模型的陆上风力机塔架虚拟传感

IF 2.4 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE DataCentric Engineering Pub Date : 2022-11-28 DOI:10.1017/dce.2022.38
Joaquin Bilbao, E. Lourens, A. Schulze, L. Ziegler
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引用次数: 3

摘要

风力发电塔架承受高度变化的内部荷载,具有很大的不确定性。不确定性源于许多因素,包括随着时间的推移,实际风场将会是什么,在有和没有控制器交互的情况下,涡轮的各种运行状态下建模的不确定性,气动阻尼的影响等等。为了监测真实的经验载荷和评估疲劳,应变传感器可以安装在涡轮结构的疲劳临界位置。一种更经济、更实用的方法是仅根据若干加速度测量来预测结构的应变响应。在这一贡献中,采用了一种方法,其中使用高斯过程潜在力模型预测了现有陆上风力涡轮机塔架的动态应变。利用该模型,根据加速度数据估计了所施加的动载荷和应变响应。利用安装在塔底附近的应变片验证了预测的动态应变。随后,通过将计算的损伤等效载荷与预测的损伤等效载荷与测量的应变进行比较来评估疲劳。结果证实了该方法对陆上风力发电机组塔架疲劳寿命消耗连续跟踪的有效性。
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Virtual sensing in an onshore wind turbine tower using a Gaussian process latent force model
Abstract Wind turbine towers are subjected to highly varying internal loads, characterized by large uncertainty. The uncertainty stems from many factors, including what the actual wind fields experienced over time will be, modeling uncertainties given the various operational states of the turbine with and without controller interaction, the influence of aerodynamic damping, and so forth. To monitor the true experienced loading and assess the fatigue, strain sensors can be installed at fatigue-critical locations on the turbine structure. A more cost-effective and practical solution is to predict the strain response of the structure based only on a number of acceleration measurements. In this contribution, an approach is followed where the dynamic strains in an existing onshore wind turbine tower are predicted using a Gaussian process latent force model. By employing this model, both the applied dynamic loading and strain response are estimated based on the acceleration data. The predicted dynamic strains are validated using strain gauges installed near the bottom of the tower. Fatigue is subsequently assessed by comparing the damage equivalent loads calculated with the predicted as opposed to the measured strains. The results confirm the usefulness of the method for continuous tracking of fatigue life consumption in onshore wind turbine towers.
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来源期刊
DataCentric Engineering
DataCentric Engineering Engineering-General Engineering
CiteScore
5.60
自引率
0.00%
发文量
26
审稿时长
12 weeks
期刊最新文献
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