通过基于自我注意的动态图表示学习和变量级归一化流程进行风力涡轮机故障检测和识别

IF 9.4 1区 工程技术 Q1 ENGINEERING, INDUSTRIAL
Yunyi Zhu , Bin Xie , Anqi Wang , Zheng Qian
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引用次数: 0

摘要

有效的风力涡轮机(WT)状态监测对于提高风力发电效率、降低运行和维护成本具有重要意义。监控和数据采集(SCADA)数据因其低成本和可访问性而被广泛用于风电机组状态监测。然而,SCADA 变量之间错综复杂的相互依存关系影响了风电机组故障检测的准确性,而且很少有方法能识别异常原因。为了解决这些问题,本文提出了一种基于自注意的动态图表示学习和变量级归一化流程的无监督故障检测和识别方法。首先,本文提出了一种基于空间和时间自注意机制的动态图表示学习模型。它能有效地学习变量之间的动态关系和相互关系,从而实现早期故障检测。其次,提出了一种变量级归一化流程,用于变量的判别密度估计,从而实现组件故障定位。最后,提出了基于对比图的节点偏差指数,以识别异常的根本原因。利用中国西北某风电场的风电数据进行的实验结果证明,所提出的方法在风电故障检测和识别方面具有更好的准确性和可解释性,在实际风能应用中显示出更好的效果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Wind turbine fault detection and identification via self-attention-based dynamic graph representation learning and variable-level normalizing flow
Effective wind turbine (WT) condition monitoring is significant to improve wind power generation efficiency and reduce operation and maintenance costs. Supervisory control and data acquisition (SCADA) data are widely utilized for WT condition monitoring due to their low cost and accessibility. However, the intricate interdependencies among SCADA variables affect the accuracy of WT fault detection, and few methods provide identification for the anomaly cause. To solve these issues, this paper proposes an unsupervised fault detection and identification method based on self-attention-based dynamic graph representation learning and variable-level normalizing flow. Firstly, a dynamic graph representation learning model based on spatial and temporal self-attention mechanisms is proposed. It can effectively learn the dynamic and mutual relations among variables for early fault detection. Secondly, a variable-level normalizing flow is proposed for discriminative density estimation of variables, which can realize component fault localization. Finally, a node deviation index based on contrast graph is proposed to identify the root cause of anomalies. Experimental results using WT data from a wind farm in Northwest China prove that the proposed method has better accuracy and interpretability in WT fault detection and identification, which displays better effectiveness in practical wind energy applications.
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来源期刊
Reliability Engineering & System Safety
Reliability Engineering & System Safety 管理科学-工程:工业
CiteScore
15.20
自引率
39.50%
发文量
621
审稿时长
67 days
期刊介绍: Elsevier publishes Reliability Engineering & System Safety in association with the European Safety and Reliability Association and the Safety Engineering and Risk Analysis Division. The international journal is devoted to developing and applying methods to enhance the safety and reliability of complex technological systems, like nuclear power plants, chemical plants, hazardous waste facilities, space systems, offshore and maritime systems, transportation systems, constructed infrastructure, and manufacturing plants. The journal normally publishes only articles that involve the analysis of substantive problems related to the reliability of complex systems or present techniques and/or theoretical results that have a discernable relationship to the solution of such problems. An important aim is to balance academic material and practical applications.
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