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一种用于风力涡轮机结构的数据驱动诊断框架：一种整体方法。

A Data-Driven Diagnostic Framework for Wind Turbine Structures: A Holistic Approach.

作者信息

Bogoevska Simona, Spiridonakos Minas, Chatzi Eleni, Dumova-Jovanoska Elena, Höffer Rudiger

机构信息

Faculty of Civil Engineering, University Ss. Cyril and Methodius, Skopje 1000, Macedonia.

Department of Civil, Environmental and Geomatic Engineering, ETH, Zürich CH-8093, Switzerland.

出版信息

Sensors (Basel). 2017 Mar 30;17(4):720. doi: 10.3390/s17040720.

DOI:10.3390/s17040720
PMID:28358346
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5421680/
Abstract

The complex dynamics of operational wind turbine (WT) structures challenges the applicability of existing structural health monitoring (SHM) strategies for condition assessment. At the center of Europe's renewable energy strategic planning, WT systems call for implementation of strategies that may describe the WT behavior in its complete operational spectrum. The framework proposed in this paper relies on the symbiotic treatment of acting environmental/operational variables and the monitored vibration response of the structure. The approach aims at accurate simulation of the temporal variability characterizing the WT dynamics, and subsequently at the tracking of the evolution of this variability in a longer-term horizon. The bi-component analysis tool is applied on long-term data, collected as part of continuous monitoring campaigns on two actual operating WT structures located in different sites in Germany. The obtained data-driven structural models verify the potential of the proposed strategy for development of an automated SHM diagnostic tool.

摘要

运行中的风力涡轮机（WT）结构的复杂动力学对现有结构健康监测（SHM）策略用于状态评估的适用性提出了挑战。作为欧洲可再生能源战略规划的核心，WT系统要求实施能够描述WT在其完整运行范围内行为的策略。本文提出的框架依赖于对作用的环境/运行变量与结构监测到的振动响应进行共生处理。该方法旨在精确模拟表征WT动力学的时间变异性，并随后在更长的时间范围内跟踪这种变异性的演变。双分量分析工具应用于长期数据，这些数据是在德国不同地点对两个实际运行的WT结构进行连续监测活动的一部分。所获得的数据驱动结构模型验证了所提出策略用于开发自动化SHM诊断工具的潜力。

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