The safe and stable operation of substation equipment is becoming more and more important, especially in recent years, the construction of smart grids has correspondingly increased its requirements. Because of the different locations and severity of substation equipment failure, infrared thermal imaging technology can quantitatively determine the hidden danger or the degree of failure. The practice has proved that this is an effective means of substation equipment fault diagnosis. In the working environment of power equipment, there will be a situation that the equipment will be covered, which will lead to the absence of targets in infrared imaging. In view of this situation, this paper randomly chooses some normal samples, constructs different missing levels and puts them into the test set for experiments. It also compares the classification effects of various algorithms in different missing levels. The results indicate that the classification accuracy of this paper is significantly lower than that of normal conditions, and it is still superior to its similar algorithms under different sample missing levels. The results also show that the method is robust and the Zernike moment invariants have certain adaptability when the target region is destroyed. It is of great significance to enhance the operation safety and economic benefits of substation equipment, reduce the maintenance cost of equipment and economic losses, and ensure the reliable operation of substation equipment.
With the construction of a large number of substations, the display problem of high-temperature equipment has been concerned. White light and infrared image registration and fusion method based on NSCT transform are designed to realize the display of high-temperature equipment in the substation. The white light and infrared images are enhanced based on NSCT. After the two images are calibrated, the white light and infrared images are registered by combining feature registration and edge detection methods. An image fusion algorithm is designed by combining wavelet transform and IHS transform to realize the fusion processing of two images. The test results indicate that the average gradient, information entropy, spatial frequency, and standard deviation of the designed method are higher. The image registration and fusion effect are in line with the human subjective vision, and the registration and fusion effect is better.
Visible and infrared cameras can solve the practical problem of high-resolution image recognition in complex operation scenes of substations, but the prerequisite is that visible and infrared cameras need to be calibrated effectively. In this paper, a calibration method of visible light and infrared camera based on edge detection is proposed. The method adopts a visible light camera and an infrared camera to shoot two images of the same calibration plate, namely a visible light image and an infrared image, and implements preprocessing. It detects the edges of two images, extracts feature point description information by using a scale invariant feature conversion algorithm and determines four groups of determined matching point pairs. It solves a transformation model by combining an internal parameter matrix to obtain an external parameter matrix and realizes visible light and infrared camera calibration. The results verify that the parallel equipotential error is less than zero. One under the application of the method, which indicates that the calibration effect of the method is better.
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