基于改进算法ANFIS的变压器故障诊断

Diagnosis of Transformer Faults Based on Improved ANFIS

在分析变压器油中溶解气体进行变压器故障诊断的基础上,提出一种基于改进算法的自适应神经模糊推理系统的变压器绝缘故障诊断方法.该方法使用IEC三比值法的3个气体比值作为ANFIS输入向量,构造三输入一输出的ANFIS,然后使用具有全局收敛性的相关的广义Fletcher-Reeves共轭梯度法改进ANFIS默认的以BP算法和最小二乘法构成的混合学习算法,再使用新的学习算法训练系统.最后,对模型的有效性进行了检验,并与使用BP学习算法训练的诊断结果做了比较.检验结果表明:使用改进算法的ANFIS进行变压器故障诊断是可行的,并且诊断精度有所提高.

Based on dissolved gas analysis （DGA） in transformer oil, an adaptive neuro-fuzzy inference system （ANFIS） based transformer insulation fault diagnosis method is proposed in this paper. The system uses the three gas ratios of IEC Three-ratio Method as input vectors to construct three-input and one-output ANFIS, and then it is trained by hybrid learning arithmetic improved by Fletcher-Reeves conjugate gradient method. Finally, the availability of ANFIS is tested, and compared with BP trained ANFIS. The results .show that the method based on ANFIS is feasible and the diagnosis accuracy is enhanced in identifying transformer fauhs.