Application of Hyperspectral Image Recognition Technology in Monitoring the Pollution Level of Insulators

Abstract

Abstract The pivotal aspect of ensuring the secure and stable functioning of Electrical Power Systems (EPS) lies in the online monitoring of transmission line insulator pollution levels, aimed at forestalling pollution flashover incidents. These flashovers typically stem from the buildup of contaminants on insulator surfaces, which, under humid conditions, establish a conductive layer, gravely jeopardizing grid security. Traditional pollution detection approaches, inclusive of manual inspections and offline sampling analyses, suffer from drawbacks like inefficiency, inadequate real-time capabilities, and vulnerability to human intervention, thereby struggling to align with the demands of contemporary power grid intelligence and automation. In view of this, this article innovatively proposes an insulator pollution monitoring model based on hyperspectral image recognition technology. This model fully utilizes the unique spectral integration feature of hyperspectral images, which can provide rich spectral data while obtaining image information, covering a wide spectral range from visible light to infrared and even more, and has extremely high spectral resolution. The experimental results show that the model not only significantly improves the accuracy and real-time performance of pollution detection, but also overcomes many shortcomings of traditional methods.