Independent Component Analysis and Manifold Learning with Applications to Fault Diagnosis of VSC-HVDC Systems

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Independent Component Analysis and Manifold Learning with Applications to Fault Diagnosis of VSC-HVDC Systems

更新时间：2025-07-09

- Independent Component Analysis and Manifold Learning with Applications to Fault Diagnosis of VSC-HVDC Systems
- Vol. 45, Issue 2, Pages: 44-48+58(2011)
- 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：
- DOI：
  CLC： TP273
- Online First：10 February 2011，
  
  Published：2011
- 稿件说明：
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Independent Component Analysis and Manifold Learning with Applications to Fault Diagnosis of VSC-HVDC Systems[J]. 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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Keywords

references

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