Transformer High-Voltage Primary Coil Quality Detection Method Based on Machine Vision

Abstract

Aiming at the problems of low efficiency and low accuracy in manual detection of winding angle and wire spacing during automatic winding of high-voltage primary coils of transmission and distribution transformers, a detection scheme using machine vision is proposed. Firstly, the coil image is acquired by the industrial camera, the detection region is segmented, and the ROI (region of interest) image is pre-processed. For winding angle detection, we propose a slicing method for image graying to reduce the interference caused by uneven light irradiation. The gray image is converted to a binary image, and wire skeleton extraction is performed; the skeleton is identified using the Hough transform for feature straight lines, and the winding angle is then calculated. For wire spacing detection, we propose an intersection of the perpendicular lines method, which extracts edge coordinates using contour images and performs endpoint pixel expansion and shape classification. Use the intersection of the vertical lines to determine the centroid coordinates of the wire outline, calculate the pixel distance of the adjacent centroid, and obtain the wire spacing by combining pixel calibration. Comparison experiments have shown that the solution has a high detection accuracy (0.01 mm), and the error of the integrated detection results is not higher than 10%, which enables the real-time detection of coil winding status and corrects the winding process according to the visual real-time detection result to improve the finished product quality of coils.