Abstract
In recent years, infrared imaging has become an important tool, particularly for predicting and preventing electrical equipment failure. Systems for online monitoring of the equipment conditions used in electrical substations are based on computer vision algorithms to perform visual analysis, automatically detect and assess equipment condition. This article describes a developed method that automatically finds defects in high-voltage insulators using infrared images. This method is based on the Otsu method, which is one of the most popular and effective segmentation methods that can be applied to finding defects in infrared images. The result is a comparative analysis of computer vision methods in infrared images used in our research. Automatic condition monitoring to find defects in high-voltage insulators in infrared images can be considered as the base method for an automated thermal imaging system for monitoring electrical substation equipment.
Highlights
Monitoring of electrical equipment or any of its components has become a challenging and important task. This is because of different equipment condition, which can result in various types of problems with the electrical equipment, but can be identified by detecting infrared radiation [1]
Degradation of electrical equipment and its components can cause overheating, which can lead to subsequent equipment failure, and can lead to unplanned power outages, possible injury and/or fire hazards
Condition monitoring systems are based on continuous infrared image processing to assess the condition of electrical equipment [3]
Summary
Monitoring of electrical equipment or any of its components has become a challenging and important task. For the detection of “hot” spots in infrared images, computer vision methods are based on segmentation, clustering, searching for threshold values [4] The purpose of these methods is to divide the image into its component parts to search for a region of interest to find equipment, background, faults and defects [5]. This article presents an automatic condition monitoring method for finding defects in high-voltage insulators in infrared images This method is based on computer vision methods [6] for searching for a region of interest of images using Otsu segmentation method. Comparative analysis of computer vision methods to search for defects in high-voltage insulators in infrared images The result of this experiment demonstrated effectiveness of the Otsu method for identifying objects and qualitatively detecting the defects themselves [8]. Defining hotspots using a threshold value – determining the threshold value using the Otsu method and identifying hotspots in the infrared image
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