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Abnormal Detection of Wind Turbine Based on SCADA Data Mining

机译：基于SCADA数据挖掘的风力涡轮机异常检测

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摘要

In order to reduce the curse of dimensionality of massive data from SCADA (Supervisory Control and Data Acquisition) system and remove data redundancy, the grey correlation algorithm is used to extract the eigenvectors of monitoring data. The eigenvectors are used as input vectors and the monitoring variables related to the unit state as output vectors. The genetic algorithm and cross validation method are used to optimize the parameters of the support vector regression (SVR) model. A high precision prediction is carried out, and a reasonable threshold is set up to alarm the fault. The condition monitoring of the wind turbine is realized. The effectiveness of the method is verified by using the actual fault data of a wind farm.
机译：为了减少来自SCADA（监控和数据采集）系统的大规模数据的维度诅咒并删除数据冗余，使用灰色相关算法来提取监视数据的特征向量。特征向量用作输入向量和与单位状态相关的监测变量作为输出矢量。遗传算法和交叉验证方法用于优化支持向量回归（SVR）模型的参数。执行高精度预测，并建立合理的阈值以报警故障。实现了风力涡轮机的状态监测。通过使用风电场的实际故障数据来验证该方法的有效性。

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  • 来源
    《Mathematical Problems in Engineering》 |2019年第17期|5976843.1-5976843.10|共10页
  • 作者

    Tao Liang; Qian Siqi; Zhang Yingjuan; Shi Huan;

  • 作者单位

    Hebei Univ Technol Coll Artificial Intelligence & Data Sci Tianjin Peoples R China;

    Hebei Univ Technol Coll Artificial Intelligence & Data Sci Tianjin Peoples R China;

    Hebei Univ Technol Coll Artificial Intelligence & Data Sci Tianjin Peoples R China;

    Hebei Univ Technol Coll Artificial Intelligence & Data Sci Tianjin Peoples R China;

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