학술논문
基于局部放电相位图谱和油中溶解气体信息融合的油纸绝缘缺陷识别方法 / Defect recognition method of oil-paper insulation based on information fusion of PRPD spectrum and dissolved gas data
Document Type
Academic Journal
Author
Source
绝缘材料 / Insulating Materials. 56(12):43-53
Subject
Language
Chinese
ISSN
1009-9239
Abstract
基于单一检测手段的变压器故障诊断方法难以对油纸绝缘的同一类型缺陷进行细化识别,无法满足深远海风电快速发展背景下电力系统对设备运行可靠性的要求.因此,本文提出了一种基于局部放电相位(PRPD)图谱和油中溶解气体分析(DGA)信息融合的油纸绝缘缺陷识别方法,设计并制作了6种电极模型,模拟变压器中不同电场不均匀系数的沿面放电典型缺陷,并采集其PRPD及DGA数据;分别采用卷积神经网络(CNN)和反向传播神经网络(BPNN)对6类缺陷的PRPD图谱和DGA特征向量进行模式识别;提出基于D-S证据理论的CNN-BPNN信息融合模型,实现基于PRPD图谱与DGA数据的联合诊断.结果表明:基于D-S证据理论的CNN-BPNN模型可有效纠正单一判据模型的错误输出,并降低分类结果的不确定度,当PRPD图谱输入维度为8×8、16×16、32×32时,融入DGA特征向量的模型识别准确率分别为93.21%、97.53%、99.17%,较PRPD图谱单一判据模型的识别准确率分别提升了4.81%、2.78%、0.84%,该模型可有效融合局部放电的电气物理信息和化学产物信息,既提高了缺陷识别准确率,又增强了输出结果的置信程度,且降低了数据存储要求,可为变压器智能运维提供精确、可靠、轻量的缺陷识别方法.
The transformer fault diagnosis technique based on a single detection method is difficult to identify the same type of defects of oil-paper insulation in detail,which cannot meet the requirements of power system on equipment operation reliability under the background of rapid development of deep offshore wind power.Therefore,an oil-paper insulation defect identification method based on information fusion of phase-resolved partial discharge(PRPD)spectrum and dissolved gas analysis(DGA)data was proposed.Six kinds of electrode models were designed and made to simulate the typical defects of surface discharge in transformers with different electric field inhomogeneity coefficients,and PRPD and DGA data were collected.Then convolutional neural network(CNN)and back propagation neural network(BPNN)were adopted to recognize the patterns of PRPD spectrum and DGA feature vector of six kinds of defects,respectively.Finally,the CNN-BPNN information fusion model based on D-S evidence theory was proposed to realize joint diagnosis based on PRPD spectrum and DGA data.The results show that the CNN-BPNN model based on the D-S evidence theory can effectively correct the wrong output of the single criterion model and reduce the uncertainty of the classification results.When the input dimensions of PRPD spectrum are 8×8,16×16,and 32×32,the recognition accuracy of the model integrated with the DGA feature vector is 93.21%,97.53%,and 99.17%,respectively,which is 4.81%,2.78%,and 0.84% higher than that of PRPD single criterion model.The CNN-BPNN model can effectively integrate the electrical physical information and chemical product information of partial discharge,which not only improves the accuracy of defect identification,but also enhances the confidence of the output results,and reduces the data storage requirements,providing accurate,reliable,and lightweight defect identification methods for intelligent operation and maintenance of transformers.
The transformer fault diagnosis technique based on a single detection method is difficult to identify the same type of defects of oil-paper insulation in detail,which cannot meet the requirements of power system on equipment operation reliability under the background of rapid development of deep offshore wind power.Therefore,an oil-paper insulation defect identification method based on information fusion of phase-resolved partial discharge(PRPD)spectrum and dissolved gas analysis(DGA)data was proposed.Six kinds of electrode models were designed and made to simulate the typical defects of surface discharge in transformers with different electric field inhomogeneity coefficients,and PRPD and DGA data were collected.Then convolutional neural network(CNN)and back propagation neural network(BPNN)were adopted to recognize the patterns of PRPD spectrum and DGA feature vector of six kinds of defects,respectively.Finally,the CNN-BPNN information fusion model based on D-S evidence theory was proposed to realize joint diagnosis based on PRPD spectrum and DGA data.The results show that the CNN-BPNN model based on the D-S evidence theory can effectively correct the wrong output of the single criterion model and reduce the uncertainty of the classification results.When the input dimensions of PRPD spectrum are 8×8,16×16,and 32×32,the recognition accuracy of the model integrated with the DGA feature vector is 93.21%,97.53%,and 99.17%,respectively,which is 4.81%,2.78%,and 0.84% higher than that of PRPD single criterion model.The CNN-BPNN model can effectively integrate the electrical physical information and chemical product information of partial discharge,which not only improves the accuracy of defect identification,but also enhances the confidence of the output results,and reduces the data storage requirements,providing accurate,reliable,and lightweight defect identification methods for intelligent operation and maintenance of transformers.