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Hierarchical method for wind turbine prognosis using SCADA data

机译：基于SCADA数据的风力涡轮机预测的分层方法

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摘要

Rapid development of wind energy requires effective wind turbine prognosis methods, which can give alarm before actual failure happens and hence enables condition-based maintenance. A hierarchical method based on GP (Gaussian Processes) and PCA (Principal Component Analysis) is proposed in this paper for turbine prognosis using SCADA data. The method includes two levels of prognosis: 1) detect which wind turbine behaves abnormally and has potential defect; 2) determine the defective components in the abnormal turbine. On turbine level, the relationship between selected parameters and power generation is trained based on GP. Then the model residual, which is calculated as the difference between the estimated output and the actually measured power, can indicate whether the turbine is defective. On component level, the contribution of each SCADA variable to turbine abnormality can be given based on PCA method, and can be used for indicating the defective components. Field dataset including 24 failed turbines is used to validate the proposed hierarchical method. The validation results show that the proposed method can achieve wind turbine prognosis with 79% detection rate on turbine level and 76% detection rate on component level. Moreover, the method can provide several months ahead alarm before severe failure happens.
机译：快速发展的风能需要有效的风力涡轮机预测方法，该方法可以在实际故障发生之前发出警报，从而实现基于状态的维护。本文提出了一种基于GP（高斯过程）和PCA（主成分分析）的分层方法，用于基于SCADA数据的涡轮机预测。该方法包括两个预后级别：1）检测哪个风力发电机异常运行并存在潜在缺陷； 2）确定异常涡轮中的有缺陷组件。在涡轮机级别，基于GP训练选定参数与发电之间的关系。然后，作为估计的输出与实际测量的功率之差计算出的模型残差可以指示涡轮是否有故障。在组件级别，可以基于PCA方法给出每个SCADA变量对涡轮机异常的影响，并可用于指示有缺陷的组件。包含24个故障涡轮的现场数据集用于验证所提出的分层方法。验证结果表明，所提出的方法可以实现风力涡轮机的预后，涡轮机水平的检测率为79％，部件水平的检测率为76％。而且，该方法可以在严重故障发生之前提前几个月提供警报。

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