摘要
针对变压器故障中数据呈现非线性,故障类型复杂,神经网络存在局部极值等问题,提出了一种改进的核Fisher(KFDA)诊断方法。在核Fisher的基础上,用欧氏距离对类间距离进行加权,一定程度上降低了数据投影重叠的问题,提升分类性能。另外,针对单一核函数的不足,采用了复合核函数,使其具有更好的非线性处理数据能力。经实验验证,KFDA分类器不存在局部最值,具有识别正确率高等优点,是一种有效的故障诊断方法。
An improved nuclear Fisher(KFDA)diagnosis method is proposed to solve the problems of non-linear data,complex fault types and local extremum in the neural network in transformer faults.On the basis of nuclear Fisher,the distance between classes is weighted by Euclidean distance,which reduces the problem of overlapping data projection to some extent and improves the classification performance.In addition,for the shortage of single kernel function,compound kernel function is used to make it has better nonlinear data processing ability.It is proved by experiments that the KFDA classifier does not has local maximum value and has the advantage of high recognition accuracy,which is an effective fault diagnosis method.
作者
宋玉琴
张建
SONG Yuqin;ZHANG Jian(College of Electronics and Information Engineering,Xi’an Polytechnic University,Xi’an 710048,China)
出处
《传感器与微系统》
CSCD
2020年第3期153-156,160,共5页
Transducer and Microsystem Technologies
基金
陕西省教育厅科研计划资助项目(15JK1312)
陕西省教育厅专项科研计划资助项目(18JK0358)
西安市科技计划资助项目(CXY1517(1))
2018年西安市科技计划项目(201805030YD8CG14(17))
中国纺织工业联合会科技指导性资助项目(2017070)。
关键词
变压器故障诊断
改进核Fisher(KFDA)
改进欧氏距离
复合核函数
transformer fault diagnosis
improved kernel Fisher discriminant analysis(KFDA)
improved Euclidean distance
complex kernel function
