Abst'/> Condition monitoring and fault detection in wind turbines based on cointegration analysis of SCADA data
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Condition monitoring and fault detection in wind turbines based on cointegration analysis of SCADA data

机译:基于SCADA数据协整分析的风机状态监测与故障检测

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AbstractThis paper presents a new methodology – based on cointegration analysis of Supervisory Control And Data Acquisition (SCADA) data – for condition monitoring and fault diagnosis of wind turbines. Analysis of cointegration residuals – obtained from cointegration process of wind turbine data – is used for operational condition monitoring and automated fault and/or abnormal condition detection. The proposed method is validated using the experimental data acquired from a wind turbine drivetrain with a nominal power of 2 MW under varying environmental and operational conditions. A two-stage cointegration-based procedure is performed on six process parameters of the wind turbine, where data trends have nonlinear characteristics. The method is tested using two case studies with known faults. The results demonstrate that the proposed method can effectively analyse nonlinear data trends, continuously monitor the wind turbine and reliably detect abnormal problems.HighlightsNew method based on cointegration analysis of SCADA data is proposed for condition monitoring of wind turbines.The method is illustrated using two experimental case studies from wind turbine data.The method can effectively analyse nonlinear data trends, continuously monitor the wind turbine and reliably detect abnormal problems.
机译: 摘要 本文提出了一种新方法-基于监督控制和数据采集(SCADA)数据的协整分析-用于状态监视和故障诊断风力涡轮机。从风力涡轮机数据的协整过程中获得的协整残差分析可用于运行状态监视以及自动故障和/或异常状态检测。在变化的环境和运行条件下,使用标称功率为2兆瓦的风力涡轮机传动系统获得的实验数据验证了该方法的有效性。对风力涡轮机的六个过程参数执行基于两步协整的过程,其中数据趋势具有非线性特征。使用两个已知故障案例研究对该方法进行了测试。结果表明,该方法能够有效地分析非线性数据趋势,对风机进行连续监测,并能够可靠地发现异常问题。 突出显示 •< / ce:label> 提出了一种基于SCADA数据协整分析的新方法,用于风力发电机的状态监测。 该方法使用两个实验案例进行了说明

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