基于最小二乘支持向量机的变压器故障诊断

介绍了一种基于最小二乘支持向量机(LS-SVM)的电力变压器故障诊断方法,将样本数据进行归一化处理,以绝缘油中特征气体种类及其含量为依据建立变压器故障诊断LS-SVM模型,对模型中的核参数σ与惩罚参数C进行优化,并将测试样本输入训练好的LS-SVM模型,得到诊断结果。实例结果分析表明,LS-SVM将原先的非线性问题转化为求解线性问题,即使在小训练样本的前提下,也能获得更为准确的诊断结果。

Fault Diagnosis of Power Transformers Based on Least Squares Support Vector Machine

Introduction was made to a kind of power transformer fault diagnosis method based on least squares support vector machine (LS-SVM). The sample data was carried out normalization processing. On the basis of the characteristic gas type and its content of the in-sulating oil, this paper established the LS-SVM model of transformer fault diagnose and optimized the nuclear parameterσ and penalty pa-rameterC in the model, putting the test sample into the trained LS-SVM model to obtain the diagnosis results. Experimental results analysis shows that LS-SVM changes the original nonlinear problem into the solution of linear problem and the more accurate diagnosis result could be obtained even under the conditions of small training sample.