基于红外图像分割与SSA-SVM的复合绝缘子缺陷检测方法

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Abstract In order to identify the defect types of AC/DC composite insulator accurately and quickly, a defect detection method for AC/DC composite insulators based on infrared image segmentation and support vector machine (SVM) optimized by sparrow search algorithm (SSA) is proposed. Firstly, short samples of composite insulators are made, and four different types of simulated defects are set. By applying AC and DC voltage respectively, the infrared image samples of normal and defective composite insulators are measured by infrared thermal imager. Then, the insulator region is obtained by a threshold segmentation method based on the maximum variance between classes, and the infrared feature parameters is calculated and selected by the Fisher criterion. Finally, the SVM model optimized by SSA is used to identify the insulator defect types. The results show that the method has a recognition accuracy of more than 87% for the defect types of AC/DC composite insulators in the laboratory and good recognition effect for AC composite insulators in primary field application.

Key words： composite insulator AC/DC defect detection image segmentation sparrow search algorithm-support vector machine (SSA-SVM)

Received: 07 June 2021

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	Articles by authors
	DONG Yifei
	SHU Shengwen
	CHEN Cheng
	JIN Ming
	WANG Jian

Cite this article:

DONG Yifei,SHU Shengwen,CHEN Cheng等. Defect detection method for composite insulator based on infrared image segmentation and SSA-SVM algorithm[J]. Electrical Engineering, 2021, 22(11): 73-79.

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http://dqjs.cesmedia.cn/EN/Y2021/V22/I11/73

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