高压输电导线的损伤检测与故障诊断

Damaged detection and fault diagnosis for high-voltage transmission line

本文利用红外和电磁传感器分别检测铝绞线和钢芯(钢绞线)的断股故障信号,应用db4小波基对故障信号进行了小波分析,由故障信号的时频域和小波分解细节特征,构造了神经网络诊断模型的输入特征矢量。利用3层BP网络实现了导线断股故障的精确诊断,所研制的红外电磁检测仪器可由巡线机器人携载和操作。

The faults of broken steel wire and broken aluminum thread are detected respectively by means of electromagnetic and infrared sensors and the failure signal is analyzed with db4 wavelet. The characteristic vectors used as the input of neural network are constructed from the time-frequency and wavelet decomposition features of the failure signal. The faults of broken steel wire and broken aluminum thread are diagnosed precisely with 3-layer BP neural network. The designed instrument can be carried and operated by the inspection robot for high-voltage transmission line.