{"title":"SSGC-GAT: Synergistic Similarity Graph Construction Strategy Combined With GAT Network for Wind Turbine Anomaly Identification Using SCADA Data","authors":"Xiaomin Wang;Xiao Zhuang;Jian Ge;Jiawei Xiang;Di Zhou","doi":"10.1109/TIM.2024.3453323","DOIUrl":null,"url":null,"abstract":"The supervisory control and data acquisition (SCADA) system is the standard installation on large wind turbine (WT) to monitor all major WT subcomponents. By analyzing SCADA data, the anomaly of the WT can be timely identified. However, the complex coupling relationship between different sensors poses a great challenge to the high accuracy of WT anomaly identification. In this article, a novel synergistic similarity graph construction (SSGC)-graph attention network (GAT) method that integrates the SSGC strategy into GAT is proposed to realize high-accuracy anomaly identification of WT. The GAT has a strong graph data modeling capability to accurately capture important relationships between nodes. Furthermore, the proposed SSGC strategy constructs similar graph data by fusing the adjacency matrices computed by four different methods. The SSGC strategy can adaptively learn the complex relationships among multiple parameters to improve the accuracy of anomaly identification. A large number of experiments are conducted to verify the effectiveness and superiority of the proposed SSGC-GAT. The experimental results show that, compared with other several benchmark methods, the proposed SSGC-GAT has the best identification performance. In addition, the ablation experiment results demonstrate that the proposed SSGC strategy can effectively improve the accuracy of WT anomaly identification.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":null,"pages":null},"PeriodicalIF":5.6000,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Instrumentation and Measurement","FirstCategoryId":"5","ListUrlMain":"https://ieeexplore.ieee.org/document/10663447/","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0
Abstract
The supervisory control and data acquisition (SCADA) system is the standard installation on large wind turbine (WT) to monitor all major WT subcomponents. By analyzing SCADA data, the anomaly of the WT can be timely identified. However, the complex coupling relationship between different sensors poses a great challenge to the high accuracy of WT anomaly identification. In this article, a novel synergistic similarity graph construction (SSGC)-graph attention network (GAT) method that integrates the SSGC strategy into GAT is proposed to realize high-accuracy anomaly identification of WT. The GAT has a strong graph data modeling capability to accurately capture important relationships between nodes. Furthermore, the proposed SSGC strategy constructs similar graph data by fusing the adjacency matrices computed by four different methods. The SSGC strategy can adaptively learn the complex relationships among multiple parameters to improve the accuracy of anomaly identification. A large number of experiments are conducted to verify the effectiveness and superiority of the proposed SSGC-GAT. The experimental results show that, compared with other several benchmark methods, the proposed SSGC-GAT has the best identification performance. In addition, the ablation experiment results demonstrate that the proposed SSGC strategy can effectively improve the accuracy of WT anomaly identification.
期刊介绍:
Papers are sought that address innovative solutions to the development and use of electrical and electronic instruments and equipment to measure, monitor and/or record physical phenomena for the purpose of advancing measurement science, methods, functionality and applications. The scope of these papers may encompass: (1) theory, methodology, and practice of measurement; (2) design, development and evaluation of instrumentation and measurement systems and components used in generating, acquiring, conditioning and processing signals; (3) analysis, representation, display, and preservation of the information obtained from a set of measurements; and (4) scientific and technical support to establishment and maintenance of technical standards in the field of Instrumentation and Measurement.