The Identification and Diagnosis of Self-Blast Defects of Glass Insulators Based on Multi-Feature Fusion

doi:10.11930/j.issn.1004-9649.2017.05.052.07

Electric Power ›› 2017, Vol. 50 ›› Issue (5): 52-58.DOI: 10.11930/j.issn.1004-9649.2017.05.052.07

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The Identification and Diagnosis of Self-Blast Defects of Glass Insulators Based on Multi-Feature Fusion

JIANG Yuntu¹, HAN Jun², DING Jian¹, FU Hanning¹, WANG Yufu², CAO Wei²

1. State Grid Zhejiang Electric Power Company Overhaul Branch, Hangzhou 310007, China;
2. School of Communication and Information Engineering, Shanghai University, Shanghai 200444, China

Received:2017-02-17 Online:2017-05-20 Published:2017-05-26
Supported by:
This work is supported by Science and Technology Program of SGCC (No. 520626140006).

Abstract

Abstract: In order to improve the recognition accuracy of insulators in UAV inspection and effectively reduce the influence of the background texture and illumination, a new insulator recognition method is proposed, which integrates the shape, color and texture of insulators. Aimed at the off-chip defects of glass insulators, a defect-detecting method is presented, which can sense the distance between gravity centers of insulator chips, and has an recognition accuracy of insulators higher than 90%. Based on testing with numerous UVA inspection images of transmission lines, it is proved that the proposed method can effectively recognize the insulators under various complicated background conditions, and detect the off-chip defects of glass insulators.

Key words: glass insulator, insulator recognition, insulator defect diagnosis, parallel shape, saliency model

CLC Number:

TM853

JIANG Yuntu, HAN Jun, DING Jian, FU Hanning, WANG Yufu, CAO Wei. The Identification and Diagnosis of Self-Blast Defects of Glass Insulators Based on Multi-Feature Fusion[J]. Electric Power, 2017, 50(5): 52-58.

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The Identification and Diagnosis of Self-Blast Defects of Glass Insulators Based on Multi-Feature Fusion

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