基于人工鱼群算法和LS_SVM的变压器故障诊断

针对变压器故障数据的特征信息不确定性以及传统诊断方法准确率较低的问题,文中采用人工鱼群算法和最小二乘支持向量机相结合的方法来进行变压器故障诊断。将IECTC10数据库中的DGA特征气体比值作为输入,建立基于最小二乘支持向量机的变压器故障诊断模型,并运用人工鱼群算法对最小二乘支持向量机的参数进行优化选取。然后根据诊断结果,选出分类效果最佳的多比值特征参量组合。实验验证结果显示,文中所提出的诊断方法准确率可达96.67%,拥有更高的故障诊断正确率。

A Transformer Fault Diagnosis Method Integrating Artificial Fish Swarm Algorithm with Least Square Support Vector Machine

In view of the information uncertainty of transformer fault data and the low accuracy of traditional diagnostic methods, the combination of artificial fish swarm algorithm and least squares support vector machine is used to diagnose transformer fault. The DGA characteristic gas ratios of IECTC10 database is used as the input vectors, and the fault diagnosis model of transformers is designed based on LS_SVM. Meanwhile, the artificial fish swarm algorithm is utilized to optimize the parameters of the least squares support vector machine. Then, based on the diagnosis result, the multi-ratio characteristic parameter combination with the best classification effect is selected. The experimental verification results show that the accuracy of the proposed diagnostic method was up to 96.67%, and it has a higher accuracy rate of fault diagnosis.