高压电器2025，Vol.61Issue(4)：21-29,9.DOI:10.13296/j.1001-1609.hva.2025.04.003
基于长短期记忆网络的换流站设备地震损伤识别方法
Seismic Damage Identification Method for Converter Station Equipment Based on Long-short-term Memory Network
李强 1宋云海 2杨洋 1张世洪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大理局,云南 大理 671000
- 2. 中国南方电网有限责任公司超高压输电公司电力科研院,广州 510000
- 折叠
摘要
Abstract
For enhancing both accuracy and real-time capability of damage identification for UHV converter station equipment in seismic period,in this paper a damage identification method of equipment based on long short-term memory(LSTM)networks combined with wavelet scattering feature extraction is proposed.The real data containing different damage conditions is formed by simulating the acceleration response of the converter station equipment in the seismic period through finite element simulation.The wavelet scattering technology is used was to extract features from the acceleration signals so to effectively reduce noise and preserve damage-related features.These extracted fea-tures are then input into the LSTM model for damage identification.The results show that the LSTM network based on the wavelet scattering features significantly improves both speed and accuracy of damage identification compared to that directly using raw acceleration data,and the final identification accuracy of the model is up to 95%.This method improves effectively both accuracy and efficiency of seismic damage identification of converter station equipment,provides reliable technical support for health monitoring and post-disaster assessment of such power infrastructure as converter station and has broad engineering application potential.关键词
特高压换流站设备/地震损伤识别/长短期记忆网络/小波散射/深度学习Key words
UHV converter station equipment/seismic damage identification/LSTM network/wavelet scattering/deep learning引用本文复制引用
李强,宋云海,杨洋,张世洪..基于长短期记忆网络的换流站设备地震损伤识别方法[J].高压电器,2025,61(4):21-29,9.
