With the rapid development of wind farm worldwide, monitoring the status of numerous wind turbines becomes the essential work. Abnormal data in wind power curve (WPC) are quite important for wind farm operations and maintenances because they usually reveal wind turbine failures or some extreme conditions. This paper proposes a new algorithm of WPC abnormal data detection and cleaning by image thresholding based on minimization of dissimilarity-and-uncertainty-based energy (MDUE). The basic idea is to transform the scattered data into a digital image and the problem of data cleaning is turned into an image segmentation problem. For all data pixels, the confidences of being classified as normal class are computed and make up a grey level feature image. Then the optimum threshold is determined by searching through the energy space based on intensity-based class uncertainty and shape dissimilarity. Finally, the normal and three types of abnormal data are marked after applying image thresholding to the feature image. The algorithm is compared with several data-based algorithms and a recently published image-based algorithm. A large number of experiments conducted on real-world WPC data collected from 37 wind turbines in two wind farms verified the superior performance of the proposed method.

中文翻译：

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基于类别不确定性和形状不相似性的图像阈值风电曲线数据清洗

随着全球风力发电场的迅速发展，监视众多风力涡轮机的状态已成为必不可少的工作。风力功率曲线（WPC）中的异常数据对于风电场的运营和维护非常重要，因为它们通常会揭示风力涡轮机故障或某些极端情况。提出了一种基于最小化基于不一致性和不确定性的能量（MDUE）的图像阈值化WPC异常数据检测和清除的新算法。基本思想是将分散的数据转换为数字图像，而数据清理的问题则变成了图像分割问题。对于所有数据像素，计算被归类为正常类别的置信度，并构成灰度级特征图像。然后，基于基于强度的类别不确定性和形状不相似性，通过在能量空间中搜索来确定最佳阈值。最后，在对特征图像进行图像阈值处理后，对正常和三种类型的异常数据进行标记。该算法与几种基于数据的算法和最近发布的基于图像的算法进行了比较。对从两个风电场的37台风力涡轮机收集的真实WPC数据进行的大量实验证明了该方法的优越性能。