| 王雨虹,王志中.基于 RFRFE 与 ISSA-XGBoost 的变压器故障辨识方法[J].电子测量与仪器学报,2021,35(12):142-150 |
| 基于 RFRFE 与 ISSA-XGBoost 的变压器故障辨识方法 |
| Transformer fault identification method based on RFRFE and ISSA-XGBoost |
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| DOI： |
| 中文关键词: 变压器 故障诊断 RFRFE 算法 麻雀算法 XGBoost |
| 英文关键词:transformer fault diagnosis RFRFE algorithm sparrow searched algorithm extreme gradient boosting (XGBoost) |
| 基金项目:国家自然科学基金(51974151,71771111)、辽宁省教育厅科技项目(LJ2019QL015)资助 |
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| 中文摘要: |
| 针对变压器故障诊断精度低的问题,提出了随机森林-递归特征消除(RFRFE)算法与改进麻雀算法( ISSA)优化极端梯
度提升树(XGBoost)的变压器故障诊断方法。 首先以诊断精度为标准,利用 RFRFE 算法选择重要特征变量,去除冗余特征;然
后采用服从均匀分布随机调整策略和莱维飞行策略来对传统麻雀算法(SSA)进行改进,并将 ISSA 与 SSA 和粒子群算法(PSO)
进行算法性能测试,证明其分类精度和网络寻优能力均有所提升;最后使用改进的麻雀算法对 XGBoost 相关超参数进行寻优,
获取 RFRFE 与 ISSA-XGBoost 相结合的综合故障诊断模型,并与 PSO-XGBoost 和 SSA-XGBoost 故障诊断模型对比诊断效果,结
果表明 ISSA-XGBoost 故障诊断率为 91. 08%,比 PSO-XGBoost 和 SSA-XGBoost 分别提高了 9. 9%、6. 93%验证了所提方法能够有
效地提高变压器故障诊断性能。 |
| 英文摘要: |
| Aiming at the problem of low accuracy of transformer fault diagnosis, a random forest-recursive feature elimination (RFRFE)
algorithm and an improved sparrow algorithm ( ISSA) optimization of the extreme gradient boosting tree (XGBoost) transformer fault
diagnosis method are proposed. First, based on the diagnostic accuracy, the RFRFE algorithm is used to select important feature
variables to remove redundant features; then the traditional sparrow algorithm ( SSA) is improved by the uniform distribution random
adjustment strategy and the Levi flight strategy, and the ISSA and SSA and particle swarm optimization ( PSO) performs algorithm
performance testing, which proves that its classification accuracy and network optimization capabilities have been improved; finally, the
improved sparrow algorithm is used to optimize XGBoost related hyperparameters to obtain the synthesis of the combination of RFRFE and
ISSA-XGBoost. The fault diagnosis model is compared with the PSO-XGBoost and SSA-XGBoost fault diagnosis models. The results show
that the fault diagnosis rate of ISSA-XGBoost is 91. 08%, which is 9. 9% and 6. 93% higher than that of PSO-XGBoost and SSAXGBoost, respectively. The proposed method can effectively improve the performance of transformer fault diagnosis. |
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