  This paper proposes an image-based algorithm for detecting and cleaning the
wind turbine abnormal data based on wind power curve (WPC) images. The abnormal
data are categorized into three types, negative points, scattered points, and
stacked points. The proposed algorithm includes three steps, data pre-cleaning,
normal data extraction, and data marking. The negative abnormal points, whose
wind speed is greater than cut-in speed and power is below zero, are first
filtered in the data pre-cleaning step. The scatter figure of the rest wind
power data forms the WPC image and corresponding binary image. In the normal
data extraction step, the principle part of the WPC binary image, representing
the normal data, is extracted by the mathematical morphology operation (MMO).
The optimal parameter setting of MMO is determined by minimizing the
dissimilarity between the extracted principle part and the reference WPC image
based on Hu moments. In the data mark step, the pixel points of scattered and
stacked abnormal data are successively identified. The mapping relationship
between the wind power points and image pixel points is built to mark the wind
turbine normal and abnormal data. The proposed image-based algorithm is
compared with k-means, local outlier factor, combined algorithm based on change
point grouping algorithm and quartile algorithm (CA). Numerous experiments
based on 33 wind turbines from two wind farms are conducted to validate the
effectiveness, efficiency, and universality of the proposed method.
