多策略改进麻雀算法与 BiLSTM 的变压器故障诊断研究

针对变压器故障诊断精度低的问题，提出了一种多策略改进麻雀算法(MISSA)与双向长短时记忆网络(BiLSTM)的变压器故障诊断模型。基于油中溶解气体分析(DGA)技术，结合无编码比值方法提取变压器9维故障特征作为模型输入进行网络训练，输出层采用Softmax函数得到故障诊断类型；采用Logistic混沌映射、均匀分布的动态自适应权重以及动态拉普拉斯算子来对麻雀搜索算法(SSA)进行改进；在初始解集内，利用MISSA对目标超参数进行寻优，使变压器故障诊断精度最优，并结合核主成分分析(KPCA)对故障特征指标降维，加快模型收敛速度。结果表明，提出的模型诊断精度为94%与PSO-BiLSTM、GWO-BiLSTM和SSA-BiLSTM故障诊断模型相比，分别提高了11.33%、8.67%、6%,验证了本文方法能够有效地提高变压器的故障诊断性能。

Research on transformer fault diagnosis based on the improvedmulti-strategy sparrow algorithm and BiLSTM

To enhance the low precision of transformer fault diagnosis, a model based on multi-strategy improved sparrow algorithm(MISSA) and bidirectional long short-term memory network (BiLSTM) is proposed. Based on dissolved gas analysis (DGA) technologyin oil, the uncoded ratio method is used to extract 9-dimensional fault features of the transformer as the input of the model for networktraining. The Softmax function is used to obtain fault diagnosis types in the output layer. The sparrow search algorithm ( SSA) isimproved by logistic chaos mapping, uniformly distributed dynamic adaptive weights and dynamic Laplacian operator. In the initialsolution set, the multi-strategy improved Sparrow algorithm (MISSA) is used to optimize the target hyperparameters. In this way, thetransformer fault diagnosis accuracy is optimized, and the kernel principal component analysis (KPCA) is used to reduce the dimensionof fault feature indexes, and the convergence speed of the model is accelerated. Compared with PSO-BiLSTM, GWA-BiLSTM and SSABILSTM fault diagnosis models, the diagnostic accuracy of the proposed model is 94% , which is 11. 33% , 8. 67% and 6% higher thanthose of PSO-BiLSTM, GWA-BiLSTM and SSA-BiLSTM fault diagnosis models, respectively. It is verified that the proposed method caneffectively improve the performance of transformer fault diagnosis.