利用DGA-NN诊断油浸式电力变压器故障

人工神经网络以其良好的非线性映射能力广泛应用于电力变压器故障诊断。为研究反向传播神经网络(BPNN)和概率神经网络(PNN)的学习过程、网络参数选择等问题,利用Matlab的神经网络工具箱结合油中溶解气体建立了BPNN和PNN的故障诊断模型,并对其性能做了分析和对比。结果表明,两种网络均能较好地实现变压器故障的实时诊断。因初始化权值的随机性,BPNN的输出结果具有差异性,收敛速度较慢,而PNN网络结构自适应确定,可以随时添加训练样本,且训练速度较快,适合于实现变压器故障的实时诊断。相同条件下,PNN的收敛速度约为BPNN的5倍。

Fault Diagnosis of Oil-immersed Power Transformer by DGA-NN

Artificial neural network(ANN) has become a promising approach compared with conventional ratio method owing to its self-learning and adaptation capability.Following this approach,incipient fault detection in power transformers using ANN can be reduced to an association process of inputs(patterns of gases concentration) and outputs(fault type).Back-propagation Neural Network(BPNN) is one of most widely used neural network model,but the slow convergence and susceptible to network expansion restrain its future application.Another problem of BPNN is the determination of the nodes of hidden layer.Probabilistic neural network(PNN) has a fast training process to adjust weights for network training without any iterative or recursive computations.The decision boundaries and the training set can be easily modified using new data as it becomes available.The two different diagnosis models were established by Neural Network Tools Box that is available in MATLAB.The performance of two models was discussed in this paper.It is concluded from the simulation results that the convergence rapidity of PNN is five times that of BPNN approximately.