多特征聚合表征的断路器热故障诊断评级方法
Multi-feature aggregated characterization of circuit breaker thermal fault diagnosis rating methods
桑金海 1许志浩 2李红斌 3康兵 1丁贵立 1王宗耀 1张兴旺1
作者信息
- 1. 南昌工程学院电气工程学院,江西南昌 330099
- 2. 南昌工程学院电气工程学院,江西南昌 330099;华中科技大学电气与电子工程学院,湖北武汉 430074;江西博微新技术有限公司,江西南昌 330099
- 3. 华中科技大学电气与电子工程学院,湖北武汉 430074
- 折叠
摘要
针对电力设备红外热故障特征的准确评估需求,提出一种多特征聚合表征的断路器热故障诊断评级方法,并以高压断路器红外图像为实例进行数据测试.首先,在高压断路器红外图像背景分离的基础上,对设备进行精准的区域划分,提取各区域温度信息;然后运用Mean-shift和改进区域生长法融合,准确提取故障发热区域面积;其次,设计一种多维聚合表征矩阵,将同一设备发热面积、热点温度、热点温差、发热位置、两相同位温升等特征值聚合为多特征向量矩阵,并运用现场案例数据构建该向量矩阵与高压断路器故障类型、等级、处理意见的关联库;最后对350张高压断路器红外图像的1002组多特征向量进行训练测试.结果表明,该方法提取的多特征向量数据使用GWO-SVM分类器测试的F-measure和Kappa系数分别为96％和95.43％,能够实现高压断路器设备热故障的全类型诊断评级及精准定位.
Abstract
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.
关键词
红外图像/断路器/多特征向量/GWO-SVMKey words
infrared image/circuit breaker/multiple eigenvectors/GWO-SVM引用本文复制引用
出版年
2024
NETL
NSTL
万方数据