Power transformer fault diagnosis under measurement originated uncertainties
Power transformer fault diagnosis under measurement originated uncertainties
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DOI：
10.1109/tdei.2012.6396956
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发表时间：
2012-12
影响因子：
3.1
通讯作者：
Hui Ma;C. Ekanayake;T. K. Saha
中科院分区：
文献类型：
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作者：
Hui Ma;C. Ekanayake;T. K. Saha
本文讨论了诊断电力变压器的故障症状与测量起源的不确定性，这是由于样本的不精确性（即由于噪声和离群值）和类的不平衡（即样本是不均匀分布在不同的故障类型）的影响，在用于识别不同的故障类型的训练数据集产生的问题。本文提出了两种模糊支持向量机（FSVM）算法，即基于模糊c均值聚类的FSVM（FCM-FSVM）和基于核模糊c均值聚类的FSVM（KFCM-FSVM），用于处理训练数据中的噪声和离群点。为了减少训练数据中类不平衡的影响，采用了类间加权和随机过采样两种方法，并与FCM-FSVM和KFCM-FSVM相结合。案例研究表明，KFCM-FSVM算法及其变体在利用溶解气体分析（DGA）测量值进行Transformer故障诊断时具有一致的趋势，可以获得满意的分类准确率。
This paper addresses the problem of diagnosing the fault symptoms of power transformers with measurement originated uncertainties, which arise from the imprecision of samples (i.e. due to noises and outliers) and the effect of class imbalance (i.e. samples are unequally distributed between different fault types) in a training dataset used to identify different fault types. Two fuzzy support vector machine (FSVM) algorithms namely fuzzy c-means clustering-based FSVM (FCM-FSVM) and kernel fuzzy c-means clustering-based FSVM (KFCM-FSVM) have been applied in this paper to deal with any noises and outliers in training dataset. In order to reduce the effect of class imbalance in training dataset, two approaches including between-class weighting and random oversampling have been adopted and integrated with FCM-FSVM and KFCM-FSVM. The case studies show that KFCM-FSVM algorithm and its variants have consistent tendency to attain satisfied classification accuracy in transformer fault diagnosis using dissolved gas analysis (DGA) measurements.