信息物理融合下的特高压换流站状态识别技术
EHV Converter Station State Identification Technology Based on Cyber-physical Integration
肖耀辉 1余俊松 1李为明 1王玉峰 1王永平 2薛海平 2黄锴 2姚金明3
作者信息
- 1. 中国南方电网有限责任公司超高压输电公司检修试验中心,广州 510000
- 2. 南京南瑞继保电气有限公司,南京 211102
- 3. 南京邮电大学 自动化学院、人工智能学院,南京 210023
- 折叠
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.
关键词
大数据挖掘/特高压直流/多源信息融合/信息物理系统/故障辨识Key words
big data mining/UHVDC/multi-source information fusion/information physical system/fault identification引用本文复制引用
基金项目
国家自然科学基金(52207009)
中国南方电网有限责任公司科技项目(0120002021030304AS00062)
出版年
2024
NETL
NSTL
万方数据