Power Transformer Fault Classification Based on Dissolved Gas Analysis by Implementing Bootstrap and Genetic Programming
Power Transformer Fault Classification Based on Dissolved Gas Analysis by Implementing Bootstrap and Genetic Programming
复制标题
DOI：
10.1109/tsmcc.2008.2007253
复制
发表时间：
2009
期刊：
影响因子：
--
通讯作者：
A. Shintemirov;Wenhu Tang;Q. Wu
中科院分区：
文献类型：
--
作者：
A. Shintemirov;Wenhu Tang;Q. Wu
针对电力Transformer溶解气体分析（DGA）中存在的高通用性或噪声污染数据，提出了一种智能故障分类方法。为了提高变压器油中气体分析的解释精度，采用了自举和遗传程序设计方法。利用Bootstrap预处理近似均衡不同故障类别的样本数，以改善后续的GP特征提取故障分类。GP应用于建立基于收集的气体数据的每个类别的分类特征。然后，用GP提取的特征作为人工神经网络（ANN），支持向量机（SVM）和K-最近邻（KNN）分类器的故障分类的输入。的组合GP-ANN，GP-SVM，GP-KNN分类器的分类精度进行了比较，来自ANN，SVM，KNN分类器，分别。试验结果表明，所提出的预处理方法能显著提高电力Transformer故障分类的准确率。
This paper presents an intelligent fault classification approach to power transformer dissolved gas analysis (DGA), dealing with highly versatile or noise-corrupted data. Bootstrap and genetic programming (GP) are implemented to improve the interpretation accuracy for DGA of power transformers. Bootstrap preprocessing is utilized to approximately equalize the sample numbers for different fault classes to improve subsequent fault classification with GP feature extraction. GP is applied to establish classification features for each class based on the collected gas data. The features extracted with GP are then used as the inputs to artificial neural network (ANN), support vector machine (SVM) and K-nearest neighbor ( KNN) classifiers for fault classification. The classification accuracies of the combined GP-ANN, GP-SVM, and GP-KNN classifiers are compared with the ones derived from ANN, SVM, and KNN classifiers, respectively. The test results indicate that the developed preprocessing approach can significantly improve the diagnosis accuracies for power transformer fault classification.