南方电网技术2026，Vol.20Issue(2)：78-86,9.DOI:10.13648/j.cnki.issn1674-0629.2026.02.008
基于SCSSA-BiLSTM的变压器故障诊断模型
Transformer Fault Diagnosis Model Based on SCSSA-BiLSTM
摘要
Abstract
Aiming at the problems of low diagnostic accuracy and easy to fall into local optimization of sparrow search algorithm(SSA)for transformer fault diagnosis,a transformer fault diagnosis model is proposed based on the optimized by sine-cosine and Cauchy mutation sparrow search algorithm(SCSSA).Firstly,based on the dissolved gas analysis(DGA)method in oil,five feature quantities are used as inputs.Secondly,the sparrow algorithm is improved by using the positive cosine strategy and Cauchy variation strategy,and then the performance of SCSSA algorithm,SSA algorithm and grey wolf optimizer(GWO)are compared on four kinds of test functions for performance comparison and verified the superiority of SCSSA algorithm.Finally,SCSSA algorithm is used to optimize the parameters in the BiLSTM network,so as to improve the performance of BiLSTM network in transformer fault diagnosis.The experimental results show that the proposed SCSSA-BiLSTM fault diagnosis model has an integrated diagnostic ac-curacy of 95.1%,which is 7.3%,12.2%,14.6%,and 19.5%higher than the SSA-BiLSTM,GWO-BiLSTM,BiLSTM,and LSTM models,respectively,and the SCSSA-BiLSTM model has better robustness.关键词
变压器/故障诊断/麻雀搜索算法/双向长短期记忆网络/诊断精度Key words
transformer/fault diagnosis/sparrow search algorithm/bi-directional long-short term memory networks/diagnostic accuracy分类
信息技术与安全科学引用本文复制引用
汪繁荣,李州..基于SCSSA-BiLSTM的变压器故障诊断模型[J].南方电网技术,2026,20(2):78-86,9.基金项目
国家自然科学基金资助项目(61903129). Supported by the National Natural Science Foundation of China(61903129). （61903129）
