In response to the demand for accurate assessment of infrared thermal fault characteristics of power equip-ment,a multi-feature aggregated characterization of circuit breaker thermal fault diagnosis rating method is proposed,and the data test is carried out using infrared images of high-voltage circuit breakers as examples.Firstly,on the basis of the background separation of high-voltage circuit breaker infrared images,the equipment is accurately divided into regions to extract the temperature information of each region.Secondly,the Mean-shift and the improved region growth method are applied to fuse and accurately extract the area of the fault heat-emitting region.Then,a multi-dimensional aggregated characterization matrix is designed to combine the heat-emitting area,hot spot temperature,hot spot temper-ature difference,heat-emitting location,temperature rise of two identical positions of the same equipment and other ei-genvalues into a multi-feature vector matrix,and the on-site case data is adopted to construct a correlation library of this vector matrix and HV circuit breaker fault types,levels and treatment opinions.Finally,1002 sets of multi-feature vectors from 350 infrared images of high-voltage circuit breakers are trained and tested.The results show that the F-measure and Kappa coefficients of the multi-feature vector data extracted by this method using GWO-SVM classifier test are 96％and 95.43％,respectively,which can achieve the all-types of diagnostic rating and accurate localization of thermal faults in high-voltage circuit breaker equipment.
NETL
NSTL
万方数据
