摘要
针对传统变压器故障诊断过程中故障征兆与故障类型间映射关系的不确定性及模糊性问题,根据粗糙集知识与多核学习理论,构建了一种变压器多级故障诊断模型。该方法基于溶解气体分析（DGA）诊断标准,以5种特征气体及16种气体比值作为初始特征量,并利用邻域粗糙集知识按属性重要度大小获取在所诊断故障类型上高重要度的最小故障特征信息集。在深入挖掘DGA所含故障信息的基础上,建立分级故障诊断模型,以二分类支持向量机作为分类器,利用最小故障特征信息集进行多级故障诊断。此外,采用反正切变换处理各输入特征,避免了油中溶解气体长尾分布而导致的误分情况;同时,各支持向量机皆采用多核学习,以解决单核支持向量机数据敏感性强,鲁棒性低的缺陷。实例分析表明：与传统特征量相比,新提出特征量下的各诊断层准确率均能较稳定的达到88%以上,且最小运行时长可达0.337 5 s,具备提高分类精度,减小运行时间与算法结构的明显优势。另外,与传统故障诊断方法相比,该多级诊断的模型不仅能更深层次挖掘故障特征信息,降低冗余特征信息的复杂性,并且可有效提高诊断平均准确率3%以上,具有更高的准确度与可靠性。
Aiming at the uncertainty and fuzziness of the relationship between fault symptom and fault type in the process of traditional transformer fault diagnosis, we construct a multi-level fault diagnosis model of transformer based on rough set knowledge and multi-kernel learning theory. In this model, five kinds of characteristic gases and 16 kinds of gas ratios are taken as the initial characteristic parameters based on the dissolved gas analysis（DGA） diagnosis standard, and the minimum fault characteristic information set with high importance on the diagnosed fault type is obtained by the attribute of the neighborhood rough set. Based on the deep excavation of the fault information contained in the DGA, a multi-level fault diagnosis model is established, and the binary-class support vector machine is used as a classifier to diagnose the fault with the minimum fault characteristic information set. In addition, in order to avoid the erroneous situation caused by the long tail distribution of the dissolved gas in the oil, the input characteristics are treated by an arctangent transformation. At the same time, all support vector machines use multi-kernel learning to solve the defects of data sensitivity and low robustness in the single kernel support vector machine. The example analysis shows that the accuracy of each diagnostic level under the characteristics can reach more than 88%, and the minimum running time can be 0.337 5 s, which has the obvious advantages of improving the classification accuracy and reducing the running time and the algorithm structure in comparison with traditional characteristics. Secondly, compared with the traditional fault diagnosis method, the multi-level diagnosis can not only extract the fault characteristic information deeper, reduce the complexity of redundant characteristics, but also effectively improve the average accuracy more than 3%, which have higher accuracy and reliability.
作者
李春茂
周妺末
刘亚婕
高波
吴广宁
LI Chunmao;ZHOU Momo;LIU Yajie;GAO Bo;WU Guangning(School of Electrical Engineering,Southwest Jiaotong University,Chengdu 610031,China)
出处
《高电压技术》
EI
CAS
CSCD
北大核心
2018年第11期3474-3482,共9页
High Voltage Engineering
基金
国家自然科学基金(U1234202)~~
关键词
变压器
反正切变换
邻域粗糙集
特征重要度
多核支持向量机
多级故障诊断
transformer
arc tangent transformation
neighborhood rough set
characteristic importance
multiple kernelsupport vector machine
multi-level fault diagnosis
