基于多分类概率输出模型的变压器故障诊断*

针对传统的SVM方法在辨别故障特征不明确的样本时会导致误诊断的问题,提出一种基于多分类概率输出(MCPO)模型的变压器故障诊断方法。利用Sigmoid函数构建了基于SVM的MCPO模型,模型的输入为DGA数据和变压器故障类型,输出为发生每种类型故障的概率估计,通过制定相关的故障诊断判据利用故障概率信息能够有效的辨识故障特征是否明确。仿真分析结果表明,MCPO模型的诊断结果能够有效识别故障特征不显著的样本,为进一步采取合理的校正措施提供一种参考。

Fault Diagnosis of Transformers Based on Multi - classified Probability Output Model

In view of the traditional SVM method may lead to false diagnosis when the sample fault feature is unclear, a fault diagnosis method is proposed based on the multi - classified probability output model. The multi - classified probability output model is constructed by using Sigmoid function based on SVM. The input of the model is the DGA data and the fault type of transformer, the output is the probability estimate for each failure type. The fault probability information can be used to identify whether the fault feature is clear by setting the relevant evaluation standards. The example shows that the samples with unknown fault characteristics can be identified by using the diagnostic results of the MCPO model, and provide a reference for further reasonable measures.