Wind Power Curve Data Cleaning Algorithm via Image Thresholding Wind turbine data from the Supervisory Control And Data Acquisition (SCADA) system is very important for wind turbine conditional monitoring, wind power prediction and wind turbine performance evaluation. However, the SCADA data usually contains lots of abnormal data. This paper presents an imagebased algorithm for abnormal data cleaning of wind power curve (WPC) data via image thresholding. The basic idea is to build a graylevel representation of the original binary image of WPC which is able to preserve the normal part as much as possible. Therefore, the cleaning operation is then turned into a problem of image segmentation. The proposed algorithm includes the following steps: First, the scatter data is converted into a binary image. Then the median of four distances are computed from each pixel in the image to the nearest connected domain boundary along four directions, and a gray level image is generated to strengthen the normal part, in the meantime, weaken the abnormal part. For all possible threshold t, the optimal t o  which makes the smallest Hu moment based dissimilarity of the segmented normal part with a reference WPC template, is finally determined. The proposed algorithm is compared with some databased algorithms as well as an imagebased mathematical morphology operation (MMO) algorithm. Experiments carried out on WPC data of 17 wind turbines from a wind farm verified the effectiveness and accuracy of the proposed method.

Phase I Analysis of Hidden Operating Status for Wind Turbine Datadriven methods based on Supervisory Control and Data Acquisition (SCADA) becomes a recent trend for wind turbine condition monitoring. However, SCADA data are known to be of low quality due to low sampling frequency and complex turbine working dynamics. In this work, we focus on the phase I analysis of SCADA data to better understand turbinesu0027 operating status. As one of the most important characterization, the power curve is used as a benchmark to represent normal performance. A powerful distributionfree control chart is applied after the power generation is adjusted by an accurate power curve model, which explicitly takes into account the known factors that can affect turbinesu0027 performance. Informative outofcontrol segments have been revealed in real field case studies. This phase I analysis can help improve wind turbineu0027s monitoring, reliability, and maintenance for a smarter wind energy system.