基于鲁棒能量模型LS-TSVM和DGA的变压器故障诊断

鲁棒能量模型最小二乘双支持向量机作为最小二乘双支持向量机(LS-TSVM)的改进算法，训练速度快、鲁棒性好且泛化能力强。将其引入到变压器故障诊断中，并提出一种鸡群算法优化鲁棒能量模型LS-TSVM的变压器故障诊断模型。在该模型中，结合二叉树和鲁棒能量模型LS-TSVM构造多类分类器用于变压器故障类型识别，并采用搜索性能较强的鸡群算法对鲁棒能量模型LS-TSVM的参数进行优化，以使模型的诊断性能达到最佳。基于DGA的变压器故障诊断实例表明，该方法故障诊断模型精度高，诊断效果优于PSO-SVM模型。

Fault diagnosis of power transformer based on RELS-TSVM and DGA

As an improved algorithm of LS-TSVM, RELS-TSVM has a high training speed, good robustness and strong generalization ability. This paper introduces it to the fault diagnosis of transformer and proposes a fault diagnosis model based on RELS-TSVM optimized by CSO algorithm. In the model, it constructs a multi-class classifier for transformer fault type identification by combining binary tree and RELS-TSVM, and uses CSO that has strong search performance to optimize the parameters of RELS-TSVM, which would upgrade the performance of the model to the best state. The example of transformer fault diagnosis based on DGA shows that the fault diagnosis model in this paper is of high accuracy and works better than PSO-SVM model. This work is supported by National Natural Science Foundation of China (No. 51477121).