Abstract
With the rapid development of China’s electrical industry, the safe operation of electrical facilities is very important for social stability and people’s property safety. The failure detection method of conventional electrical equipment is hand detection, which has high experience of the detection person, lacks detection and error detection, and the detection efficiency is low. With the development of artificial intelligence technology, computer-assisted substation inspection is now possible, and substation inspection using an intelligent inspection robot equipped with an infrared device is one of the main substation inspection methods. In this paper, experiments are carried out using several neural network models. For example, if a faster region convolutional neural networks (RCNN) infrared detection model is employed, a good vg16 in the feature region of the extracted image takes into account the quality of the infrared image and the presence of multiple devices. Infrared images can be used to determine the basic features of various electronic devices. In order to detect targets in infrared images of electrical equipment, the fast RCNN target detection algorithm is used, and the overall recognition accuracy reaches 83.1%, and a good application effect is obtained.
Highlights
With the concept of “three types and two networks” put forward, the national power grid has continuously increased the consolidation of smart grid and the investment in grid construction and is committed to building a world-class energy network enterprise [1, 2]
Fault detection technology can find the faults of high-voltage equipment in time, which is very important for the safe operation of electrical equipment [3]
The faster region convolutional neural networks (RCNN) target detection algorithm is applied to the infrared detection of electronic equipment, and the infrared thermal detection model of fast RCNN electronic equipment is constructed, which can detect and detect more electronic equipment in infrared images and achieve good application results
Summary
With the concept of “three types and two networks” put forward, the national power grid has continuously increased the consolidation of smart grid and the investment in grid construction and is committed to building a world-class energy network enterprise [1, 2]. Is is an important means of detecting electrical equipment faults. E neural network is a new research direction in the field of artificial intelligence, which was introduced to make machine learning as close as possible to artificial intelligence [7]. We hope to use target detection, pattern recognition, and other means to analyze and process infrared images collected from cameras, infrared thermal imagers, intelligent roving robots, and other equipment, locate the positions of various electronic equipment in the images, and identify various faults in electrical equipment. Is is of great significance to ensuring the safe and stable operation of substation electrical equipment and has very important research value [11] Mathematical Problems in Engineering [10]. is is of great significance to ensuring the safe and stable operation of substation electrical equipment and has very important research value [11]
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