基于Faster R-CNN算法的变电站设备识别与缺陷检测技术研究OACSTPCD

As a transit station for power transportation,substations are an important infrastructure for city operation and life of people.During the operation of the substation,the problem of untimely detection of the temperature of the equip-ment operation due to the remote location,which does not support direct detection by robots or drones,often occurs.Tra-ditional defect recognition algorithms for substation equipment are based on machine learning algorithms,which have low accuracy,only suitable for defect detection of individual equipment categories,as well as susceptible to environmental in-fluences.On this basis,a method to recognize infrared defects of substation equipment is proposed in this paper.Firstly,equipment identification based on Faster R-CNN algorithm is used to identify the target of six types of substation equipment including bushings,insulators,wires,voltage transformers,lightning rods,and circuit breakers so as to realize the pre-cise location of the equipment;then,an algorithm based on sparse representation classification(SRC)is used to obtain the actual labels of the input samples;finally,the region of equipment is used to identifies the abnormal defects of the de-vice temperature based on the temperature threshold discriminative algorithm.The method in this paper realizes equipment recognition and defect detection under infrared images,and the accuracy of detecting infrared images of six types of equip-ment using the method designed in this paper reaches 91.58％,and the average recognition accuracy of defects of differ-ent types of equipment is 91.62％,and the recognition accuracy of the overall defect image reaches 87.62％.The experi-mental results demonstrate the effectiveness and accuracy of the proposed method.