面向智能光电复合缆的故障识别与定位研究

Fault identification and location for intelligent photoelectric composite cable

为满足智能光电复合缆对于故障识别与定位精度的需求,文中深入研究了故障监测系统中的故障识别与定位功能。对于故障识别功能,文中在故障分类与故障特征提取方法的基础上,根据故障特征提出了基于人工神经网络的故障识别方法,并通过仿真验证了所提出的方法可迅速达到精度要求。对于故障定位功能,文中在分析了自然频率与故障位置的关系之后,根据相应的线性关系提出了基于独立成分分析与模态分解的自然频率提取技术。仿真结果表明,该技术可以有效过滤信号的无用分量,从而实现精确的故障定位。

In order to meet the requirements of intelligent photoelectric composite cable for fault identification and positioning accuracy,this paper studies the fault identification and location function in the fault monitoring system. For the fault identification function,this paper first introduces the fault classification and fault feature extraction method. According to the fault characteristics,the fault identification method based on artificial neural network is proposed and verified by simulation. The proposed method can quickly meet the accuracy requirements. For the fault location function,this paper first gives the relationship between natural frequency and fault location. According to the corresponding linear relationship,this paper proposes a natural frequency extraction technique based on independent component analysis and modal decomposition. The simulation proves that the technology can effectively filter unwanted components of the signal for precise fault location.