论文标题
使用基于树的分类器检测和分类电源变压器中的内部故障
Detection and Classification of Internal Faults in Power Transformers using Tree-based Classifiers
论文作者
论文摘要
本文提出了基于决策树（DT）的检测和对电源变压器中内部故障的分类。通过改变故障电阻，故障构成角度和断层下的绕组百分比，在电源系统计算机辅助设计（PSCAD）/电磁瞬变（包括DC（EMTDC））中模拟故障。从属于时间和频域的阶段A，B和C中的差分电流中提取了一系列特征。在这些方面，选择了三个功能来区分内部故障和磁性涂片，另外三个特征将其分类为变压器的主要和次要中的故障。 DT，随机森林（RF）和梯度提升（GB）分类器用于确定断层类型。结果表明，DT检测到100 \％精度的故障，而GB分类器在三个分类器中表现最好，同时对内部故障进行分类。
This paper proposes a Decision Tree (DT) based detection and classification of internal faults in a power transformer. The faults are simulated in Power System Computer Aided Design (PSCAD)/ Electromagnetic Transients including DC (EMTDC) by varying the fault resistance, fault inception angle, and percentage of winding under fault. A series of features are extracted from the differential currents in phases a, b, and c belonging to the time, and frequency domains. Out of these, three features are selected to distinguish the internal faults from the magnetizing inrush and another three to classify faults in the primary and secondary of the transformer. DT, Random Forest (RF), and Gradient Boost (GB) classifiers are used to determine the fault types. The results show that DT detects faults with 100\% accuracy and the GB classifier performed the best among the three classifiers while classifying the internal faults.
