Image Recognition Technology with Its Application in Defect Detection and Diagnosis Analysis of Substation Equipment

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.

A review of indoor-orbital electrical inspection robots in substations

“…The artificial intelligence fault diagnosis system can identify the image to obtain the maximum temperature, and then set the corresponding thresholds for the different devices. When the temperature exceeds this threshold or there is a large difference between the threshold temperature and the measured temperature, the diagnostic system will raise the alarm (Cheng and Xiang, 2019; Allan and Beaudry, 2014; Xia et al , 2021; Luo et al , 2021; Cui et al , 2013). The flow chart of the thermal image recognition is shown in Figure 18.…”

Thermal fault diagnosis of complex electrical equipment based on infrared image recognition

“…The collective functioning and stable operation of this equipment are pivotal for ensuring the safety and reliability of power transmission. Most electrical equipment in substations is exposed to the outdoor environment, which subjects it to long-term degradation from harsh weather conditions, foreign object intrusion, frequent operation, and other factors, leading to rust, blockages, insulation degradation, or even equipment failure 2 , 3 . Statistically, failures in essential electrical equipment, such as transformers and switches, are frequently characterized by abnormal heating phenomenon, including the corrosion of the switches, poor contact of the circuit breakers, deterioration, and moisture of potential transformers, etc.…”