基于改进粒子群算法的小波神经网络在变压器故障诊断中的应用

摘要

In view of non-linear characteristics between fault symptoms and fault types of transformers, a wavelet neural network fault diagnosis model based on improved particle swarm algorithm is designed with the data of dissolved gas analysis. The model, constructed by three-layer wavelet neural networks, is trained by an improved particle swarm algorithm. By introducing the mutation operator of genetic algorithm, inertia weight factor and Gaussian-weighted global extremes on the basis of the standard particle swarm algorithm, it can accelerate the training speed of wavelet neural network and improve the accuracy of training. The simulation experiments show that this improved particle swarm algorithm wavelet neural network can be effectively applied to transformer fault diagnosis and provides a new way for transformer fault diagnosis.%针对变压器故障征兆和故障类型的非线性特性，结合油中气体分析法，设计了一种改进粒子群算法的小波神经网络故障诊断模型。模型采用3层小波神经网络，并用一种改进粒子群算法对其进行训练。该算法在标准粒子群算法的基础上，通过引入遗传算法中的变异算子、惯性权重因子和高斯加权的全局极值，加快了小波神经网络训练速度，提高了其训练的精度。仿真实验证明这种改进粒子群算法的小波神经网络可以有效地运用到变压器故障诊断中，为变压器故障诊断提供了一条新途径。