基于IGSO优化LM网络的变压器故障诊断方法

针对现今电力变压器故障诊断方法中存在编码边界区间过于绝对、准确率不高等一系列问题,提出了一种自适应搜索萤火虫算法（IGSO）优化列文伯格·马夸尔特（Levenberg Maquardt,LM）网络的变压器故障诊断方法.该方法采用萤火虫个体代表神经网络的权值和阈值、LM网络的均方误差函数作为萤火虫个体的适应度函数,利用改进萤火虫算法迭代寻优得到LM网络的最优权值和阈值.同时,运用模糊理论对改良三比值法的边界模糊化,将得到的特征气体比值编码作为网络模型的输入,不仅有利于去除冗余信息,并且克服了编码边界区间过于绝对的缺点.然后,建立基于自适应搜索萤火虫算法优化的神经网络模型,并将典型变压器故障数据代入仿真,通过与贝叶斯正则化神经网络模型以及粒子群模型的仿真结果对比,表明该方法具有较好的分类效果,准确率达到88.57％.

Power Transformer Fault Diagnosis Based on IGSO Optimization Algorithm

In view of the problems existing in power transformer fault diagnosis,such as the code boundary is too absolute and the accuracy is low,a new power transformer fault diagnosis method is proposed,which applies the self-adaptive search improved glowworm swarm optimization （IGSO） to optimize the LM neural network.The method adopts the firefly individuals as the neural network＇s weights and thresholds and the mean square error function of neural network as the individuals＇ fitness function,and uses the IGSO to obtain the optimal weights and thresholds of the LM neural network.In the meantime,the fuzzy theory is used to handle the boundary of the improved three ratio method,and the obtained characteristic gas ratio code is used as the network model input,which has the advantages to remove the redundant information and overcome the absoluteness of code boundary.In the end,the LM neural network model of GSO algorithm is established based on self-adaptive search,and the transformer fault data is inputted for simulation.A comparison of the simulation results of the Bias regularization neural network model and the particle swarm model shows that the method has good classification performance with an accuracy rate of 88.57％.