基于多级支持向量机分类器的电力变压器故障识别

支持向量机是以统计学习理论为基础发展起来的新的通用学习方法．较好地解决了小样本、高维数、非线性等学习问题。提出了一种基于多级支持向量机分类器的电力变压器故障识别方法。该方法首先通过特殊数值处理过程，对色谱分析法检测到的特征气体含量进行数值预处理。提取出故障识别所需要的6个特征量。然后利用数值预处理后得到的数据样本分别对三级支持向量机进行训练和识别。并最后判断输出变压器所处的状态。测试结果表明，该方法具有三个优点：1)具有较强的鲁棒性。识别正确率极高；2)训练时间很短，实时性能好；3)不存在局部极小问题。

Fault Diagnosis of Power Transformer Based on Multi-Layer SVM Classifier

Support Vector Machine (SVMs) is a novel machine learning method based on statis tical learning theory (SLT). SVM is powerful for the problem with small sample, nonlinear and high dimension. A multi-layer SVM classifier is applied here to f ault diagnosis of power transformer. Through a special data dealing process, con tents of five characteristic gases obtained by DGA are transformed, and 6 charac teristic components for fault diagnosis are distilled for SVMs. The multi-layer SVM classifier, trained with the sampling data from the above dealing process, identifies out the four types of transformer states. The test results show that the proposed classifier has an excellent performance on training speed and corre ct ratio.