{"title":"风电机组隐藏运行状态分析","authors":"Yuchen Shi, Nan Chen","doi":"10.1109/IEEM44572.2019.8978833","DOIUrl":null,"url":null,"abstract":"Data-driven methods based on Supervisory Control and Data Acquisition (SCADA) becomes a recent trend for wind turbine condition monitoring. However, SCADA data are known to be of low quality due to low sampling frequency and complex turbine working dynamics. In this work, we focus on the phase I analysis of SCADA data to better understand turbines' operating status. As one of the most important characterization, the power curve is used as a benchmark to represent normal performance. A powerful distribution-free control chart is applied after the power generation is adjusted by an accurate power curve model, which explicitly takes into account the known factors that can affect turbines' performance. Informative out-of-control segments have been revealed in real field case studies. This phase I analysis can help improve wind turbine's monitoring, reliability, and maintenance for a smarter wind energy system.","PeriodicalId":255418,"journal":{"name":"2019 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Phase I Analysis of Hidden Operating Status for Wind Turbine\",\"authors\":\"Yuchen Shi, Nan Chen\",\"doi\":\"10.1109/IEEM44572.2019.8978833\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Data-driven methods based on Supervisory Control and Data Acquisition (SCADA) becomes a recent trend for wind turbine condition monitoring. However, SCADA data are known to be of low quality due to low sampling frequency and complex turbine working dynamics. In this work, we focus on the phase I analysis of SCADA data to better understand turbines' operating status. As one of the most important characterization, the power curve is used as a benchmark to represent normal performance. A powerful distribution-free control chart is applied after the power generation is adjusted by an accurate power curve model, which explicitly takes into account the known factors that can affect turbines' performance. Informative out-of-control segments have been revealed in real field case studies. This phase I analysis can help improve wind turbine's monitoring, reliability, and maintenance for a smarter wind energy system.\",\"PeriodicalId\":255418,\"journal\":{\"name\":\"2019 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IEEM44572.2019.8978833\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IEEM44572.2019.8978833","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
摘要
基于SCADA (Supervisory Control and Data Acquisition)的数据驱动方法已成为风电机组状态监测的发展趋势。然而,由于采样频率低和涡轮工作动态复杂,SCADA数据的质量很低。在这项工作中,我们将重点放在SCADA数据的第一阶段分析上,以更好地了解涡轮机的运行状态。作为最重要的特性之一,功率曲线被用作表示正常性能的基准。通过精确的功率曲线模型对发电量进行调整后,应用了功能强大的无分布控制图,该控制图明确考虑了已知的影响涡轮机性能的因素。在实际的现场案例研究中揭示了信息失控部分。第一阶段的分析可以帮助改善风力涡轮机的监测、可靠性和维护,以实现更智能的风能系统。
Phase I Analysis of Hidden Operating Status for Wind Turbine
Data-driven methods based on Supervisory Control and Data Acquisition (SCADA) becomes a recent trend for wind turbine condition monitoring. However, SCADA data are known to be of low quality due to low sampling frequency and complex turbine working dynamics. In this work, we focus on the phase I analysis of SCADA data to better understand turbines' operating status. As one of the most important characterization, the power curve is used as a benchmark to represent normal performance. A powerful distribution-free control chart is applied after the power generation is adjusted by an accurate power curve model, which explicitly takes into account the known factors that can affect turbines' performance. Informative out-of-control segments have been revealed in real field case studies. This phase I analysis can help improve wind turbine's monitoring, reliability, and maintenance for a smarter wind energy system.