独立分量分析和流形学习在VSC-HVDC系统故障诊断中的应用

李志雄; 严新平

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独立分量分析和流形学习在VSC-HVDC系统故障诊断中的应用

更新时间：2025-07-09

- 独立分量分析和流形学习在VSC-HVDC系统故障诊断中的应用
- Independent Component Analysis and Manifold Learning with Applications to Fault Diagnosis of VSC-HVDC Systems
- 西安交通大学学报 2011年45卷第2期页码：44-48+58
- 作者机构：
  
  武汉理工大学能源与动力工程学院,武汉,430063
- 作者简介：
- 基金信息：
  
  : 2010-07-08. 作者简介: 李志雄(1983-),男,博士生;严新平(联系人),男,教授,博士生导师. 基金项目:国家自然科学基金资助项目(50975213);高等学校学科创新引智计划资助项目(B08031)
- DOI：
  中图分类号： TP273
- 网络首发：2011-02-10，
  
  纸质出版：2011
- 稿件说明：
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李志雄, 严新平. 独立分量分析和流形学习在VSC-HVDC系统故障诊断中的应用[J]. 西安交通大学学报, 2011,45(2):44-48+58.

Independent Component Analysis and Manifold Learning with Applications to Fault Diagnosis of VSC-HVDC Systems[J]. 2011, 45(2): 44-48+58.
李志雄, 严新平. 独立分量分析和流形学习在VSC-HVDC系统故障诊断中的应用[J]. 西安交通大学学报, 2011,45(2):44-48+58. DOI：

Independent Component Analysis and Manifold Learning with Applications to Fault Diagnosis of VSC-HVDC Systems[J]. 2011, 45(2): 44-48+58. DOI：

摘要

提出一种基于独立分量分析(ICA)和局部线性嵌入流形学习算法(LLE)的新型高压直流输电(VSC-HVDC)系统故障诊断方法.由于随机噪声的干扰

单个传感器测得的系统故障信号无法直接用于故障检测

故使用快速ICA对多通道传感器测得的直流电压和电流信号进行盲源分离处理以恢复去噪的系统故障源信号; 然后利用LLE挖掘潜藏于恢复信号中的子流形

提取故障敏感特征; 最后将LLE提取的故障特征量作为支持向量机(SVM)的输入

建立系统故障诊断模型.通过对系统交流相对相故障、交流相对地故障以及复合故障等仿真信号进行分析

表明所提出的ICA-LLE方法能够有效地提取故障关键特征

并在3维空间将故障特征隔离

从而得到满意的SVM故障识别效果

且SVM分类精度比只使用LLE提高了近20%.

Abstract

A fault diagnosis scheme for voltage source converter-high voltage direct current transmission(VSC-HVDC)system is proposed based on the independent component analysis(ICA)and the locally linear embedding(LLE)in the present work. The measured signals in the VSC-HVDC can not be used directly to detect system fault due to the heavy inference noise. The FastICA is hence employed to eliminate the disturbed noise and recover the fault sources from the measured DC line voltage and current observation signals. Then the LLE algorithm is applied to extract distinct characteristics hiding in the recovered fault signals. To enhance the fault pattern recognition

the support vector machine(SVM)is adopted to learn the relationship between the fault features and the system operation conditions. The ability of the proposed ICA-LLE method to detect VSC-HVDC system fault is evaluated with the simulated data. The analysis results demonstrate the feasibility and effectiveness. The distinguished features of the fault signals

such as AC line-to-line fault and AC line-to-ground fault

and the compound faults

can be extracted efficiently and then isolated in the 3-D feature space correctly. The classification rate of the SVM with the proposed scheme is increased by 20% compared with LLE.

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references

WEIMERS L. HVDC light: a new technology for a better environment[J]. IEEE Power Engineering Review, 1998, 18(8): 19-20.

颜秉勇,刘喜梅,田作华,等. 协同滤波器和支持向量机在HVDC系统故障诊断中的应用[J]. 中国电机工程学报, 2008, 28(1): 23-29.

YAN Bingyong, LIU Ximei, TIAN Zuohua, et al. Application of consensus filter and SVM in fault diagnosis for HVDC systems [J]. Proceedings of the CSEE, 2008, 28(1): 23-29.

NARENDRA K G,SOOD V K,KHORASANI K, et al. Application of a radial basis function(RBF)neural network for fault diagnosis in a HVDC system[J]. IEEE Trans on Power Systems,1998,13(1):177-183.

LAI L L,NDEH-CHE F,CHARI T. HVDC system fault diagnosis with neural networks[C]∥Fifth European Conference on Power Electronics and Applications. Brighton, UK:[s.n.],1993:145-150.

张家奇, 吴坚, 于飞. 基于神经网络的HVDC系统故障诊断[J]. 高电压技术, 2006, 32(5): 65-68.

ZHANG Jiaqi, WU Jian, YU Fei. Neural network based fault diagnosis in HVDC system [J]. High Voltage Engineering, 2006, 32(5): 65-68.

于飞, 李升娟, 吕冬梅,等. 支持向量机在高压直流输电系统故障诊断中的应用[J]. 仪器仪表学报, 2006, 27(6): 1734-1736.

YU Fei, LI Shengjuan, LÜ Dongmei, et al. Application of support vector machine for fault diagnosis in a HVDC system [J]. Chinese Journal of Scientific Instrument, 2006, 27(6): 1734-1736.

张建明, 曾建武, 谢磊,等. 基于粗糙集的支持向量机故障诊断[J]. 清华大学学报: 自然科学版, 2007, 47(S2): 1174-1177.

ZHANG Jianming, ZENG Jianwu, XIE Lei, et al. Application of support vector machine for fault diagnosis in a HVDC system [J]. Journal of Tsinghua University: Science Technology, 2007, 47(S2): 1174-1177.

HYVÄRINEN A. Fast and robust fixed-point algorithm for independent component analysis [J]. IEEE Trans on Neural Network, 1999,10(3):626-634.

司文荣, 李军浩, 梁永春,等. 采用独立成分分析的局部放电脉冲双通道提取技术研究[J]. 西安交通大学学报, 2008, 42(10): 1264-1269.

SI Wenrong, LI Junhao, LIANG Yongchun, et al. Pulse extraction for partial discharge with double channels with independent component analysis [J]. Journal of Xi'an Jiaotong University, 2008, 42(10): 1264-1269.

高军峰, 郑崇勋, 王沛. 基于独立成分分析和流形学习的眼电伪差去除[J]. 西安交通大学学报, 2010, 44(2): 113-118.

GAO Junfeng, ZHENG Chongxun, WANG Pei. Real-time removal of ocular artifacts from EEG signals using ICA and manifold algorithm [J]. Journal of Xi'an Jiaotong University, 2010, 44(2): 113-118.

ROWEIS S, SAUL L. Nonlinear dimensionality reduction by locally linear embedding [J]. Science, 2000, 290(5500): 2323-2326.

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