现代电力2024，Vol.41Issue(4)：793-800,8.DOI:10.19725/j.cnki.1007-2322.2022.0278
基于多尺度特征提取-改进天鹰算法-长短时神经网络的有载分接开关故障诊断方法
Fault Diagnosis Method for On-load Tap Changer Based on Multiscale Feature Extraction and IAO-LSTM
龚禹璐 1崔龙飞 2王典浪 1陈静 1须雷 2皮天满 1谢正波 1杨继翔1
作者信息
- 1. 中国南方电网有限责任公司超高压输电公司曲靖局,云南省曲靖市 655000
- 2. 南京南瑞继保工程技术有限公司,江苏省南京市 210000
- 折叠
摘要
Abstract
To realize the accurate fault diagnosis of on-load tap changer(OLTC)under compound faults,a fault diagnosis method for transformer OLTC based on multi-scale feature ex-traction and IAO-LSTM was proposed.Firstly,features of the time domain scale,frequency domain scale and energy entropy scale were extracted from OLTC vibration signals to form fea-ture vectors.By incorporating the mixing initialization strategy and elite solution retention strategy,the aquila optimizer(AO)was improved to enhance the convergence.The improved aquila optimizer(IAO)was used to optimize the number of hid-den layer nodes and learning rate of LSTM,and thus an optim-al LSTM model was obtained.Taking the fusion eigenvector of the single fault and compound fault as the input and the fault state as the output,the optimal model was trained.After that,the fault diagnosis was carried out.The results indicate that the method yields an average accuracy of 97.2% and is appropriate for OLTC fault diagnosis.关键词
有载分接开关/多尺度特征提取/优化LSTM神经网络/改进天鹰算法/故障诊断Key words
on-load tap changer/multi-scale feature extrac-tion/optimize LSTM neural network/improved aquila op-timizer/fault diagnosis分类
信息技术与安全科学引用本文复制引用
龚禹璐,崔龙飞,王典浪,陈静,须雷,皮天满,谢正波,杨继翔..基于多尺度特征提取-改进天鹰算法-长短时神经网络的有载分接开关故障诊断方法[J].现代电力,2024,41(4):793-800,8.
