A Fast Measurement Method of Overhead Line Temperature

Optical fiber composite transmission lines and submarine cables are often combined with optical fibers, and the temperature can be measured by Brillouin optical time-domain reflectometry (BOTDR). In order to improve the real-time performance for temperature measurement, an artificial neural network (ANN) is employed to calculate Brillouin frequency shift and demodulate fiber temperature according to it. Different signal-to-noise ratios’ Brillouin spectra of optical fiber composite overhead line are generated by simulation. The spectrum fitting algorithm using pseudo-Voigt model and the artificial neural network proposed in this work are used to demodulated the temperature. The calculation results reveal that ANN has a similar accuracy with that of the spectrum fitting algorithm using pseudo-Voigt model, however the computation burden of the former is 1/971 of the latter. ANN proposed in this work provides a fast and high precision method for temperature measurement of overhead lines.