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

作　　者：李强 宋云海[2] 杨洋 张世洪 LI Qiang;SONG Yunhai;YANG Yang;ZHANG Shihong(CSG Extra High Voltage Power Transmission Company,Dali Bureau,Yunnan Dali 671000,China;Maintenance and Test Center of CSG EHV Power Transmission Company,Guangzhou 510000,China)

机构地区：[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

出　　处：《高压电器》2025年第4期21-29,共9页High Voltage Apparatus

摘　　要：为了提高特高压换流站设备在地震中的损伤识别精度和实时性,文中提出了一种基于长短期记忆网络(LSTM)结合小波散射特征提取的设备损伤识别方法。通过有限元仿真模拟换流站设备在地震中的加速度响应,生成包含不同损伤工况的时程数据。采用小波散射技术对加速度信号进行特征提取,以减少信号噪声并保留损伤特征,再将提取后的特征输入LSTM模型进行损伤识别。结果表明,基于小波散射特征的LSTM网络相比直接使用原始加速度数据,显著提升了识别速度与精度,模型的最终识别准确率达到95%。该方法有效提高了换流站设备地震损伤识别的准确性和效率,为换流站等电力基础设施的健康监测和灾后评估提供了可靠的技术支持,并具有广泛的工程应用潜力。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 

分 类 号：TM721[电气工程—电力系统及自动化]