改进BP神经网络的SVM变压器故障诊断

油中溶解气体是变压器故障诊断的重要依据，为了融合以及扩充变压器油中溶解气体含量的特征信息，提高变压器故障诊断准确率，本文提出了改进BP神经网络的SVM（Support Vector Machine）变压器故障诊断方法。首先，通过改进的BP神经网络将5维的气体特征信息进行融合并扩充到128维；然后，在改进的BP神经网络中使用每层提取的特征向量作为SVM的输入对变压器故障进行诊断，增加改进的BP神经网络中诊断准确率较高的特征向量的权重；最后，选择累积权重最大的特征向量作为输入，使用SVM进行变压器的故障诊断。该方法经过多层神经网络的映射使提取的气体特征信息融合及扩充后具有更加明显的特征区别，从而可以有效的提高SVM的诊断准确率。实验结果表明，本文所提出的算法与BP神经网络和SVM的变压器故障诊断方法相比诊断准确率有较大的提升。同时，随着训练数据样本的增加，模型的诊断准确率具有一定的提升。

Fault diagnosis of support vector machine transformer based on improved BP neural network

Dissolved gas in oil is an important basis for transformer fault diagnosis. In order to fuse and expand the characteristic properties of dissolved gas content in transformer oil and improve the accuracy of transformer fault diagnosis, this paper proposes a support vector machine transformer fault diagnosis method based on Improved BP neural network. Firstly, the five-dimensional gas feature attributes are fused and extended to 128 dimensions by Improving BP neural network. Then, using the extracted feature vectors of each layer as the input of the support vector machine to diagnose the transformer fault and increase the Improved nerve The weight of the feature vector with higher diagnostic accuracy in the network; finally, the feature vector with the largest cumulative weight is selected as the input, and the support vector machine is used for fault diagnosis of the transformer. The method is mapped by multi-layer neural network to make the extracted gas feature information merge and expand to have more obvious feature differences, which can effectively improve the diagnostic accuracy of the support vector machine. The simulation results show that the proposed algorithm has a higher diagnostic accuracy than the neural network and support vector machine transformer fault diagnosis method. At the same time, with the increase of training data samples, the diagnostic accuracy of the model has a greater improvement.