Farm‐wide interface fatigue loads estimation: A data‐driven approach based on accelerometers

Wind Energy Pub Date : 2024-02-15 DOI:10.1002/we.2888
Francisco de N Santos, N. Noppe, W. Weijtjens, C. Devriendt
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Abstract

Fatigue has become a major consideration factor in modern offshore wind farms as optimized design codes, and a lack of lifetime reserve has made continuous fatigue life monitoring become an operational concern. In this contribution, we discuss a data‐driven methodology for farm‐wide tower‐transition piece fatigue load estimation. We specifically debate the employment of this methodology in a real‐world farm‐wide setting and the implications of continuous monitoring. With reliable nacelle‐installed accelerometer data at all locations, along with the customary 10‐min supervisory control and data acquisition (SCADA) statistics and three strain gauge‐instrumented 'fleet‐leaders', we discuss the value of two distinct approaches: use of either fleet‐leader or population‐based data for training a physics‐guided neural network model with a built‐in conservative bias, with the latter taking precedence. In the context of continuous monitoring, we touch on the importance of data imputation, working under the assumption that if data are missing, then its fatigue loads should be modeled as under idling. With this knowledge at hand, we analyzed the errors of the trained model over a period of 9 months, with monthly accumulated errors always kept below . A particular focus was given to performance under high loads, where higher errors were found. The cause for this error was identified as being inherent to the use of 10‐min statistics, but mitigation strategies have been identified. Finally, the farm‐wide results are presented on fatigue load estimation, which allowed to identify outliers, whose behavior we correlated with the operational conditions. Finally, the continuous data‐driven, population‐based approach here presented can serve as a springboard for further lifetime‐based decision‐making.
全农场界面疲劳载荷估算:基于加速度计的数据驱动方法
随着设计规范的优化,疲劳已成为现代海上风电场的一个主要考虑因素,而寿命储备的缺乏又使持续疲劳寿命监测成为一个运营问题。在本文中,我们讨论了一种数据驱动方法,用于全风电场塔筒过渡件疲劳载荷估算。我们特别讨论了该方法在全农场实际环境中的应用以及持续监测的意义。通过在所有位置安装可靠的机舱加速度计数据,以及惯用的 10 分钟监控和数据采集 (SCADA) 统计数据和三个应变计仪器 "机群领导",我们讨论了两种不同方法的价值:使用机群领导或基于群体的数据来训练具有内置保守偏差的物理引导神经网络模型,后者优先。在连续监测方面,我们提到了数据估算的重要性,并假设如果数据缺失,则其疲劳载荷应建模为怠速状态下的疲劳载荷。有了这些知识,我们对训练有素的模型进行了为期 9 个月的误差分析,每月累计误差始终保持在 。我们特别关注了高负荷下的性能,发现在高负荷下误差更大。造成这种误差的原因被认为是使用 10 分钟统计所固有的,但我们也找到了缓解策略。最后,介绍了整个农场的疲劳负荷估算结果,从而确定了异常值,我们将其行为与运行条件相关联。最后,这里介绍的基于群体的连续数据驱动方法可以作为进一步基于寿命决策的跳板。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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