基于特征气体关联特征的变压器故障诊断方法

Transformer Fault Diagnosis Method Based on Association Characteristics of Characteristic Gases

现有的基于油中溶解气体分析(dissolved gases analysis, DGA)的变压器故障诊断方法未能充分挖掘不同故障下特征气体间的关联特征。基于此,论文提出一种基于故障特征气体间关联特征的变压器故障诊断方法。利用最大信息系数(maximal information coefficient, MIC)方法定量表征不同故障类型下特征气体间的关联程度,并使用受试者工作特征(receiveroperatingcharacteristic,ROC)曲线获得不同故障类型下特征气体间的关联特征量及其分布范围,进而建立变压器故障诊断模糊推理系统;针对实际使用中数据采集周期较长的问题,对比选取了牛顿插值方法扩大待识别样本数据,保证了提取特征的有效性。针对选取的故障时序数据,论文所提方法的故障诊断准确率达到了100%,远高于三比值法和大卫三角法,说明论文所提方法充分地利用了故障特征气体间的关联特征,有效地提升了基于DGA的变压器故障诊断方法的性能,为基于DGA的变压器故障诊断提供了一种新的特征。

Existing transformer fault diagnosis methods based on dissolved gases analysis(DGA) fail to make full use of association relationship between characteristic gases in different kinds of faults. We proposed a transformer fault diagnosis method based on association characteristics between different gases. The maximal information coefficient(MIC) method was used for the quantitative characterization of the association relationship between characteristic gases, the receiver operating characteristic(ROC) curve was used to extract the association characteristics between characteristic gases and their distribution ranges, and the fuzzy inference system was established. Furthermore, in order to avoid long data-acquisition cycle in actual use, the Newton interpolation method was used to expand the size of data samples for diagnosis, which can help improve the effectiveness of characteristics. The accuracy of the method proposed on the time series data for test is 100%, which is much higher than that of the three-ratio method and David triangle method. The results illustrate that the proposed method makes full use of association relationship between characteristic gases, and can improve the performance of DGA based methods. Thus, a new kind of characteristics can be provided for transformer fault diagnosis method based on DGA.