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Wind Turbine Condition Monitoring Using SCADA Data and Data Mining Method

机译:使用SCADA数据和数据挖掘方法的风力发电机状态监测

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WTCM (Wind Turbine Condition Monitoring) system is important for wind farm operators to realize condition-based O &M (operation & maintenance), in the purpose of reducing O &M cost and improving wind turbine reliability. A WTCM method using only SCADA data based on data mining algorithm is proposed in this paper. Firstly, ARD (Automatic Relevance Determination) algorithm is adopted to determine the effective variables that are relevant to wind turbine condition. Feature vector is then extracted using the effective variables to represent the operation condition of wind turbine. Finally, the condition of a wind turbine is determined using outlier detection algorithm based on the extracted feature vector. Real-world dataset is used to validate the efficiency of the proposed method. Experiment results show that the proposed method can provide advanced failure alarm for wind turbines many days before failure happens. O &M cost can be reduced by condition-based O &M strategy using the result of our proposed WTCM method.
机译:WTCM(风轮机状态监测)系统对于风电场运营商实现基于状态的运维(运营和维护)非常重要,目的是降低运维成本并提高风机的可靠性。提出了一种基于数据挖掘算法的仅使用SCADA数据的WTCM方法。首先,采用ARD(自动相关性确定)算法来确定与风力发电机状态相关的有效变量。然后使用有效变量提取特征向量,以表示风力涡轮机的运行状况。最后,基于提取的特征向量,使用异常值检测算法确定风力涡轮机的状态。实际数据集用于验证所提出方法的效率。实验结果表明,该方法可以在故障发生前多天为风机提供高级故障报警。使用我们提出的WTCM方法的结果,可以通过基于条件的运维策略来降低运维成本。

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