作者:肖耀辉，余俊松，李为明，王玉峰，王永平，薛海平，黄锴，姚金明
Authors:XIAO Yaohui,YU Junsong,LI Weiming,WANG Yufeng,WANG Yongping,XUE Haiping,HUANG Kai,YAO Jinming摘要:由于特高压换流站系统数据来源广泛、采集密度高、量测装置多样、通信协议复杂，现有技术难以对换流站复杂状态及其隐含的故障特征进行准确辨识 。 因此本文提出了基于信息物理融合的特高压换流站特征识别技术，在对以图像为主的多源异构数据进行预处理与关联分析后，基于信息物理双侧状态运行及迁移特征关联矩阵，对换流站物理与通信双侧故障进行分析、训练与识别 。基于实际换流站监控图像进行了实例分析和方法对比， 结果表明该方法优于一些传统的故障诊断与特征识别算法，具有较好的诊断能力。
Abstract:Due to the wide range of data sources ， high collection density ， diverse measurement devices and complex communication protocols in the EHV converter station system，it is difficult for the existing technology to accurately identify the complex states of the converter station and its implied fault characteristics. Therefore，this paper proposes an information-physical fusion-based feature identification technology for EHV converter stations. After pre-processing and correlation analysis of image-based multi-source heterogeneous data，the analysis，training and identification of physical and communication faults of converter stations are performed based on the cyber-physical dual-side state operation and migration feature correlation matrix. Example analysis and method comparison based on actual converter station monitoring images are conducted，and the results show that the method outperforms some traditional fault diagnosis and feature identification algorithms and has better diagnostic capability.

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