Power transmission line stockbridge damper falling-off defect detection method

Abstract

The invention discloses a power transmission line stockbridge damper falling-off defect detection method which has high stockbridge damper falling-off defect detection accuracy and does not need a lotof training samples. The power transmission line stockbridge damper falling-off defect detection method comprises stockbridge damper marking, template screening, offset estimation and compensation ofan image to be detected, stockbridge damper target extraction, stockbridge damper feature extraction, and defect determination based on similarity analysis. By employing a method for analyzing the similarity between the image to be detected and a historical template image, the problem that the demanded number of the training samples is large is avoided; at the same time, by leading in a multi-template comparison technology and an image position offset estimation and compensation technology, the detection accuracy is further improved, and via actual validation, the accuracy is higher than 90%.The power transmission line stockbridge damper falling-off defect detection method is suitable for promotion and application in the technical field of power transmission line detection.

Description

technical field [0001] The invention relates to the technical field of transmission line detection, in particular to a method for detecting the fall-off defect of a shockproof hammer of a transmission line. Background technique [0002] The transmission line is an important part of the power system, and it undertakes the task of long-distance transmission of electric energy. The anti-vibration hammer in the power line has been exposed to the natural environment for a long time. It not only bears its own damage such as natural tension and material aging, but also suffers from external erosion such as lightning strikes, storms, and rain. If there is a phenomenon that the anti-shock hammer falls off in the transmission line, the wire will vibrate strongly under the action of strong wind, and the wire will be fatigued and damaged after long-term periodic bending, which has serious safety hazards. The traditional line inspection of transmission lines relies on manual inspection,...

AI Technical Summary

Problems solved by technology

If there is a phenomenon that the anti-shock hammer falls off in the transmission line, the wire will vibrate strongly under the action of strong wind, and the wire will be fatigued and damaged after long-term periodic bending, which has serious potential safety hazards

The traditional line inspection of transmission lines relies on manual inspection, but the efficiency of manual inspection is low, the work intensity is high, and for special lines, especially high-voltage lines, there is a risk to the personal safety of the inspectors.

The existing anti-vibration hammer drop-off defect detection method is mainly based on machine learning. First, the existing anti-vibration hammer drop-off defect detection method based on machine learning requires tens of thousands or hundreds of thousands of marked images (training samples) of anti-vibration hammer. , the accuracy rate of the existing machine learning-based anti-vibration hammer drop-off defect detection is difficult to reach 80%

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