A novel condition monitoring method based on the adaptive multivariate control charts and the supervisory control and data acquisition (SCADA) system is developed. Two types of control charts are adopted: one is the adaptive exponential weighted moving average (AEWMA) control chart for abnormal state detection, and the other is the multivariate exponential weighted moving average (MEWMA) control chart for anomaly location determination. Optimization procedures for these control charts are implemented to achieve minimum out-of-control average running length. Multivariate regression analysis is utilized to obtain the normal condition prediction model of wind turbine with fault-free SCADA data. After comparing the regression accuracy of several popular algorithms in the MRA, the random forest is adopted for feature selection and regression prediction. Various tests on the wind turbine with normal and abnormal states are conducted. The performance and robustness of various control charts are compared comprehensively. Compared with conventional control charts, the AEWMA control chart is more sensitive to the abnormal state and thus has a more effective anomaly identification ability and better robustness. It is shown that the MEWMA control chart combined with the out-of-limit number index can effectively locate and identify the abnormal component.

中文翻译：

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基于自适应多元控制图和SCADA数据的风力发电机组状态监测新方法

提出了一种基于自适应多元控制图和监督控制与数据采集（SCADA）系统的状态监测新方法。采用两种类型的控制图：一种是用于异常状态检测的自适应指数加权移动平均（AEWMA）控制图，另一种是用于异常位置确定的多元指数加权移动平均（MEWMA）控制图。实施这些控制图的优化程序可实现最小失控平均运行时间。利用多元回归分析获得了无故障SCADA数据的风机正常状态预测模型。在比较了MRA中几种常用算法的回归精度之后，采用随机森林进行特征选择和回归预测。在具有正常和异常状态的风力涡轮机上进行了各种测试。全面比较了各种控制图的性能和鲁棒性。与常规控制图相比，AEWMA控制图对异常状态更敏感，因此具有更有效的异常识别能力和更好的鲁棒性。结果表明，MEWMA控制图与超限指数结合可以有效地定位和识别异常分量。AEWMA控制图对异常状态更敏感，因此具有更有效的异常识别能力和更好的鲁棒性。结果表明，MEWMA控制图与超限数字索引相结合可以有效地定位和识别异常分量。AEWMA控制图对异常状态更敏感，因此具有更有效的异常识别能力和更好的鲁棒性。结果表明，MEWMA控制图与超限指数结合可以有效地定位和识别异常分量。