Abstract
Detection of substation equipment can promptly and effectively discover equipment overheating defects and prevent equipment failures. Traditional manual diagnosis methods are difficult to deal with the massive infrared images generated by the autonomous inspection of substation robots and drones. At present, most of the infrared image defect recognition is based on traditional machine learning algorithms, with low recognition accuracy and poor generalization capability. Therefore, this paper develops a method for identifying infrared defects of substation equipment based on the improvement of traditional ones. First, based on the Faster RCNN, target detection is performed on 6 types of substation equipment including bushings, insulators, wires, voltage transformers, lightning rods, and circuit breakers to achieve precise positioning of the equipment. Afterwards, different classes are identified based on the sparse representation-based classification (SRC), so the actual label of the input sample can be obtained. Finally, based on the temperature threshold discriminant algorithm, defects are identified in the equipment area. The measured infrared images are used for experiments. The average detection accuracy achieved by the proposed method for the 6 types of equipment reaches 92.34%. The recognition rate of different types of equipment is 98.57%, and the defect recognition accuracy reaches 88.75%. The experimental results show the effectiveness and accuracy of the proposed method.
Highlights
Detection of substation equipment can promptly and effectively discover equipment overheating defects and prevent equipment failures
Based on the Faster region convolutional neural network (RCNN), target detection is performed on 6 types of substation equipment including bushings, insulators, wires, voltage transformers, lightning rods, and circuit breakers to achieve precise positioning of the equipment
A large number of inspections or online monitoring still require manual analysis, which is time-consuming and inefficient, and difficult to cope with the massive infrared images generated by inspections. erefore, the researchers have carried out a series of works on infrared defect recognition. ese methods used different types of features or classification models to analyze and process images and determined and recognized defects
Summary
Detection of substation equipment can promptly and effectively discover equipment overheating defects and prevent equipment failures. Based on the improvement of traditional image analysis technology, this paper proposes an infrared defect recognition method for substation equipment. Compared with the traditional image defect detection algorithms of substation equipment, this paper further introduces SRC to realize the accurate confirmation of the category of the input substation equipment Such operation has important auxiliary significance for subsequent targeted defect detection and failure analysis. The Faster RCNN detection model is trained based on the infrared image training dataset of the substation equipment to generate a defect detection network. Due to the influence of noise and the unstable error between the training and the test samples, it is difficult to achieve a completely accurate reconstruction. erefore, the sparse coefficient vector is solved as follows under the given reconstruction error limit: Test sample
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