摘要
为了判别电力变压器绕组变形状况,提出一种基于最优漏磁信息及多层聚类编码和最优权重译码的ECOC分类器对变压器绕组变形分类的方法,首先利用多层聚类算法构建最优ECOC编码矩阵,然后通过最优权重译码算法对分类器输出进行译码得出变压器绕组具体变形类别,建立变压器绕组二维漏磁场有限元模型,计算得出绕组可能出现的变形形式和绕组区域变形前后的磁感应强度值数据,从而得出磁感应强度测量点;最后利用所建立模型得出的绕组变形数据进行仿真判别。结果表明,所建立的分类器在绕组变形判别时具有较高的准确性,可用于变压器绕组变形类型的检测。
In order to determine the deformation status of power transformer windings, a classification method of the transformer windings deformation was proposed based on the magnetic field measurement and the improved ECOC classi- fier. The ECOC classifier based multi-level clustering coding and optimal weighting was proposed to classify the trans- former winding deformation. Firstly, a multi-layer clustering algorithm was used to construct the optimal ECOC coding matrix. And then the optimal weight coding algorithm was adopted to classify the output of the classifier decoding trans- formers derived from the specific types of deformation. The finite element model of two-dimensional leakage magnetic field of transformer winding was established. The possible deformation forms of the windings and the values of the mag- netic induction intensity before and after the deformation of the winding area were calculated to obtain the magnetic induc- tion intensity measurement points. The simulation results show that the established classifier has high accuracy in the de- termination of winding deformation and can be used to detect the type of transformer winding deformation.
作者
陈世彪
邓祥力
杨欢红
CHEN Shi-biao;DENG Xiang-li;YANG Huan-hong(Huadian Electric Power Research Institute,Hangzhou 310000,China;School of Electric Engineering,Shanghai University o{ Electric Power,Shanghai 200090,China)
出处
《水电能源科学》
北大核心
2018年第11期173-177,共5页
Water Resources and Power
基金
国家自然科学基金项目(51777119)
上海绿色能源并网工程技术研究中心项目(13DZ2251900)
关键词
变压器绕组
漏磁感应强度
变形分类
纠错输出编码矩阵
多层聚类
最优权重
transformer winding
magnetic flux leakage inductance
deformation classification
error correction out- put coding matrix
multilayer clustering
optimal weight