Newly designed identifying method for ice thickness on high‐voltage transmission lines via machine vision

Abstract

Abstract The icing of transmission lines will bring considerable challenges to the safe operation of the power grid. Therefore, a novel method combines machine vision and machine learning algorithms for identifying the ice thickness on high‐voltage transmission line (HVTL) as proposed herein. First, noise and background interference in the image are filtered, and the grey image is used as input. Then, the algorithms of improved Canny edge detection, Hough transform, improved K‐means clustering, and least‐squares fitting are adopted in turn to locate the edges of conductors. Finally, according to the distance mapping model based on monocular vision, the ice thickness of the conductor is determined by calculating the width difference before and after icing. The experimental results show that the proposed method can accurately locate the edge of the conductor in both field and experimental environments. Moreover, it can ensure ideal effects under different illumination and hardly not be affected by distortion in both horizontal and vertical directions. Besides, the distance mapping model can map the pixel distance to the actual distance with high precision, no matter whether the background is simple or complex, and the calculated ice thickness has only a small deviation compared to the actual value. In addition, the proposed method shows high reliability and effectiveness when various interference such as different backgrounds, uneven icing, height difference changes, conductor movement, contrast changes, and conductor sag occur.

Keywords

• computer vision
• edge detection
• Hough transforms
• learning (artificial intelligence)
• least squares approximations
• pattern clustering