基于SVM的电力变压器绕组变形类型识别
|
摘要
频响法检测电力变压器绕组变形,主要是通过高中低三个频段频率响应曲线的波峰波谷与频率的变化改变来进行判断。在检测过程中缺乏定量化判断依据,一些绕组变形类型对频响曲线的影响比较相似,故容易出现变形类型的判断失误。本文提出了基于支持向量机(SVM)的绕组变形识别方法。首先使用能够反映频响曲线整体特征的物理量(特征量),建立电力变压器绕组变形识别的SVM模型即特征量可提高绕组变形识别准确度与效率。其次针对电力变压器绕组绕组变形的三类故障即轴心偏移,幅向变形和饼间变化等,选取合适的训练样本与测试样本。最后为提高SVM模型对电力变压器绕组故障检测的准确度,找到最优惩罚因子和核函数。通过对试验样本进行识别,得到上述三类电力变压器绕组故障识别准确率。测试结果说明通过SVM建模可以很大程度上消除了人为误判的影响,实现了变压器绕组变形的准确检测。
Frequency response method was used to detect the power transformer winding deformation with the changing of peaks and troughs and frequency spectrum frequency in the curves of high middle lower spectrum. Some types of winding deformation was similar in the reflection of frequency curve. It would lead to the mistake results in the judgment of deformation by the reason of without the quantitative judgment. The paper came up with the SVM method in detecting the type of power transformer winding deformation, which can improve the recognition accuracy and efficiency of winding deformation. In order to set up the SVM model accurately of power transformer winding deformation recognition. Firstly the paper determined the characteristic, which could inflect the overall frequency response curve characteristic quantities. Secondly, the three types fault of the power transformer winding, including axis offsetting, picture to the deformation, winding changing between bread, chose the appropriate training samples and testing samples. Finally, the paper selected the optimal punishment factor and kernel function, in order to improve the accuracy of the SVM model for power transformer. The paper could get the three types fault identification accuracy of the power transformer winding with the identification of the test samples. The test results showed that the SVM model could dramatically eliminated the influence of the artificial misjudgment, and it realized the detection of transformer winding accurately.
Frequency response method was used to detect the power transformer winding deformation with the changing of peaks and troughs and frequency spectrum frequency in the curves of high middle lower spectrum. Some types of winding deformation was similar in the reflection of frequency curve. It would lead to the mistake results in the judgment of deformation by the reason of without the quantitative judgment. The paper came up with the SVM method in detecting the type of power transformer winding deformation, which can improve the recognition accuracy and efficiency of winding deformation. In order to set up the SVM model accurately of power transformer winding deformation recognition. Firstly the paper determined the characteristic, which could inflect the overall frequency response curve characteristic quantities. Secondly, the three types fault of the power transformer winding, including axis offsetting, picture to the deformation, winding changing between bread, chose the appropriate training samples and testing samples. Finally, the paper selected the optimal punishment factor and kernel function, in order to improve the accuracy of the SVM model for power transformer. The paper could get the three types fault identification accuracy of the power transformer winding with the identification of the test samples. The test results showed that the SVM model could dramatically eliminated the influence of the artificial misjudgment, and it realized the detection of transformer winding accurately.
引文
[1]陈晓晗.基于有限元法的电力变压器绕组变形检测与识别的仿真研究[D].重庆大学,2015.
[2]欧阳旭东,林春耀,等.扫频阻抗法检测变压器绕组轴向位移研究[J].绝缘材料,2016,49(08):78-81.
[3]吴书有.基于振动信号分析方法的电力变压器状态监测与故障诊断研究[D].中国科学技术大学,2009.
[4]谷红霞.电力变压器绕组松动变形与振动信号相关研究[D].昆明理工大学,2017.
[5]任和.电力变压器绕组变形识别方法的研究[D].沈阳工业大学,2009.
[6]夏付炳.电力变压器绕组变形试验频响法应用[J].中国新技术新产品,2016(14):76-77.
[2]欧阳旭东,林春耀,等.扫频阻抗法检测变压器绕组轴向位移研究[J].绝缘材料,2016,49(08):78-81.
[3]吴书有.基于振动信号分析方法的电力变压器状态监测与故障诊断研究[D].中国科学技术大学,2009.
[4]谷红霞.电力变压器绕组松动变形与振动信号相关研究[D].昆明理工大学,2017.
[5]任和.电力变压器绕组变形识别方法的研究[D].沈阳工业大学,2009.
[6]夏付炳.电力变压器绕组变形试验频响法应用[J].中国新技术新产品,2016(14):76-77.