An abnormal wind turbine data cleaning algorithm based on color space conversion and image feature detection. (1st April 2022)

• Record Type: Journal Article
• Authors: Long, Huan; Xu, Shaohui; Gu, Wei
• Is Part Of: Applied energy. Volume 311(2022)
• Publication Date: 2022-04-01
• Subjects: Wind turbine -- Abnormal data cleaning -- Wind power curve -- Hough transform -- Canny edge detection; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2022.118594
• Languages: English
• ISSNs: 0306-2619

Abstract

Abstract: Wind power curve (WPC) is established through data collected from the Supervisory Control and Data Acquisition (SCADA) system of each wind turbine, which can be used to analyze the operation status. However, numerous outliers are contained in SCADA data caused by wind turbine failures, shutdown maintenance or other extreme conditions to deform the wind power curve. This paper proposes a data cleaning algorithm for wind turbine abnormal data based on wind power curve image by color space conversion and image feature detection. Considering wind speed, wind power and data frequency, a three-dimensional (3D) WPC image is constructed. The scattered outliers are cleared by their statistical characteristics. The Canny edge detection and Hough transform are introduced to extract image features of stacked outliers and locate them accurately. The proposed algorithm is compared with three common outlier detection algorithms, including two data-based algorithms and an image-based algorithm. Extensive experiments conducted on the data of 22 wind turbines from two different wind farms in China indicate the efficiency, stability and reliability of the proposed algorithm. Graphical abstract: Highlights: The wind turbine data cleaning problem is converted into image processing problem. The 3D WPC image considers wind speed, power and data density. The proposed algorithm is insensitive to wind turbine types. No reference image is needed for stacking abnormal data cleaning. The data cleaning efficiency is not impacted by the data size.