结合KPCA的阴性选择变压器故障诊断  

Transformer Fault Diagnosis Based on Real-value Negative Selection and KPCA Method

作　　者：陈鹏 

机构地区：[1]陕西华电安康发电有限公司,陕西安康715152

出　　处：《高压电器》2015年第9期109-115,共7页High Voltage Apparatus

摘　　要：为解决常规阴性选择方法在故障诊断领域存在漏诊和故障难以有效细分检测的缺陷,文中提出一种结合核主成分分析(KPCA)的实值阴性选择自适应分类算法,采用实值编码,以欧氏距离衡量被检样本与检测器的亲和度,具备学习和分类的能力,实现了故障的细分类。针对电力变压器油中溶解气体数据(DGA),应用核主成分分析技术提取样本特征量,经归一化处理后对所提出的算法进行训练,测试结果证明了该方法的有效性,与BP神经网络、最小二乘支持向量机相比,获得了更高的故障检出率,对电力变压器典型的4类故障检出率达到了90%以上。A self-adaptive classifier which combines real-value negative selection algorithm is proposed to kernel principal component analysis （KPCA） with prevent missed diagnosis and insufficiently detailed detection in fault diagnosis. Real value encoding is employed, and Euclidean distance measure is utilized to measure the affinity of sample and detector. Thus, the classifier achieves the ability of learning and classification for the purpose of detailed classification of faults. Kernel principal component analysis technique is applied to extract the characteristic quantities of sample according to the data of dissolved gases in transformer oil, and the normalized characteristic quantities are used for training the classifying algorithm. Test results show that compared with BP neural network method and least squares support vector machine method, the proposed self-adaptive classifier obtains higher fault detection rate, and its fault detection rates for four types of power transformer faults all reach more than 90%.

关 键 词：电力变压器 故障诊断 否定选择 实值编码 油中溶解气(DGA) 核主成分分析(KPCA) 

分 类 号：TM407[电气工程—电器]