基于漏磁场检测的变压器绕组变形在线监测方法研究
本文选题:变压器 + 漏磁场 ; 参考:《华北电力大学》2017年硕士论文
【摘要】:变压器作为一种传输和变换电能的重要设备,其绕组在运行过程中受到多次外部短路电动力的冲击可能会发生不可恢复的形变,这种形变已成为引发变压器绕组匝间短路故障的首要原因,因此,对变压器绕组形变进行及时准确地在线监测具有重大的现实意义。本文根据变压器绕组发生形变后,将直接影响绕组附近的磁场分布,提出通过在线测量变压器磁场的分布,间接实现对变压器绕组形变的在线监测。首先,对变压器绕组形变与其磁场分布的对应关系进行研究。用ANSYS建立变压器绕组正常及常见的形变模型,从定性和定量角度分析变压器绕组形变与其磁场分布的对应关系。结果表明,变压器磁场分布随着绕组形变种类的不同而呈现不同的特征。其次,提出一种基于磁场分布特征的变压器绕组形变分类法。根据绕组形变前后变压器磁场的定量分析结果,定义五种用于判定变压器绕组形变类型的系数,以实现对变压器绕组形变的分类。结果表明,该方法可以直观、准确地对变压器绕组的形变进行分类。然后,提出一种基于支持向量机的变压器绕组形变分类法。用ANSYS仿真得到的变压器绕组形变样本对支持向量机进行训练和测试,采用交叉验证和网格搜索法对支持向量机的参数进行优化。根据传感器安装方案对绕组形变分类正确率的影响,用改进粒子群算法对磁场传感器安装进行优化。结果表明,该方法可以在安装较少传感器的情况下,实现对变压器绕组形变的准确分类。最后,提出对变压器绕组形变程度的判定方法。用欧式距离的大小衡量变压器绕组形变的严重程度,根据粒子群算法得到的最优传感器安装方案,通过分析变压器绕组不同类型形变的严重程度与欧式距离的关系,提出对变压器各种类型形变严重程度的判定方法,并给出变压器绕组形变的判定阈值。
[Abstract]:As an important equipment for transmitting and converting electric energy, transformer windings may undergo irrecoverable deformation when they are impacted by external short-circuit electromotive force many times during operation. This kind of deformation has become the primary cause of transformer winding inter-turn short circuit fault. Therefore, it is of great practical significance to timely and accurately monitor transformer winding deformation on line. According to the fact that the magnetic field distribution near the transformer winding will be directly affected by the deformation of the transformer winding, this paper puts forward that the on-line monitoring of the transformer winding deformation can be indirectly realized by on-line measuring the distribution of the transformer magnetic field. Firstly, the relationship between transformer winding deformation and magnetic field distribution is studied. The normal and common deformation models of transformer windings are established by ANSYS. The relationship between transformer winding deformation and magnetic field distribution is analyzed qualitatively and quantitatively. The results show that the magnetic field distribution of transformer presents different characteristics with different types of winding deformation. Secondly, a method of transformer winding deformation classification based on magnetic field distribution is proposed. According to the quantitative analysis results of transformer magnetic field before and after winding deformation, five coefficients used to judge transformer winding deformation type are defined in order to realize the classification of transformer winding deformation. The results show that the method can classify transformer winding deformation intuitively and accurately. Then, a transformer winding deformation classification method based on support vector machine (SVM) is proposed. The support vector machine is trained and tested by the transformer winding deformation samples simulated by ANSYS. The parameters of the support vector machine are optimized by cross-validation and grid search. According to the effect of sensor installation scheme on the accuracy of winding deformation classification, an improved particle swarm optimization algorithm is used to optimize the magnetic field sensor installation. The results show that the method can accurately classify transformer winding deformation with fewer sensors installed. Finally, a method to judge the degree of transformer winding deformation is proposed. The magnitude of Euclidean distance is used to measure the severity of transformer winding deformation. According to the optimal sensor installation scheme obtained by particle swarm optimization, the relationship between the severity of different types of deformation of transformer windings and Euclidean distance is analyzed. A method for judging the severity of various types of transformer deformation is presented, and the threshold value of transformer winding deformation is given.
【学位授予单位】:华北电力大学
【学位级别】:硕士
【学位授予年份】:2017
【分类号】:TM41
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