A mapping method for anomaly detection in a localized population of structures

IF 2.4 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE DataCentric Engineering Pub Date : 2022-08-09 DOI:10.1017/dce.2022.25
Weijiang Lin, K. Worden, A. E. Maguire, E. Cross
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Abstract

Abstract Population-based structural health monitoring (PBSHM) provides a means of accounting for inter-turbine correlations when solving the problem of wind farm anomaly detection. Across a wind farm, where a group of structures (turbines) is placed in close vicinity to each other, the environmental conditions and, thus, structural behavior vary in a spatiotemporal manner. Spatiotemporal trends are often overlooked in the existing data-based wind farm anomaly detection methods, because most current methods are designed for individual structures, that is, detecting anomalous behavior of a turbine based on the past behavior of the same turbine. In contrast, the idea of PBSHM involves sharing data across a population of structures and capturing the interactions between structures. This paper proposes a population-based anomaly detection method, specifically for a localized population of structures, which accounts for the spatiotemporal correlations in structural behavior. A case study from an offshore wind farm is given to demonstrate the potential of the proposed method as a wind farm performance indicator. It is concluded that the method has the potential to indicate operational anomalies caused by a range of factors across a wind farm. The method may also be useful for other tasks such as wind power and turbine load modeling.
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一种在局部结构群中进行异常检测的映射方法
摘要基于群体的结构健康监测(PBSHM)在解决风电场异常检测问题时,提供了一种计算风机间相关性的方法。在风电场中,一组结构(涡轮机)相互靠近,环境条件和结构行为以时空方式变化。在现有的基于数据的风电场异常检测方法中,时空趋势往往被忽视,因为目前的大多数方法都是针对单个结构设计的,即基于同一涡轮机的过去行为来检测涡轮机的异常行为。相比之下,PBSHM的理念涉及在结构群体中共享数据,并捕捉结构之间的相互作用。本文提出了一种基于群体的异常检测方法,特别是针对结构的局部群体,该方法考虑了结构行为中的时空相关性。通过一个海上风电场的案例研究,证明了所提出的方法作为风电场性能指标的潜力。得出的结论是,该方法有可能指示整个风电场由一系列因素引起的运行异常。该方法也可用于其他任务,例如风力发电和涡轮机负载建模。
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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
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