The Cable Temperature Monitoring System Based On Distributed Optical Fiber Signal Processing Research

Compared to the other types of temperature sensing system, the distributed optical fiber temperature sensor system has many unique advantages, such as long distance, measurement data authenticity is high, the realization of distributed measurement and so on. Therefore, distributed optical fiber temperature measurement can be as the on-line power cable temperature monitoring system of a kind of effective means, to achieve the safe and stable operation of the power grid. Now more and more scientific research personnel and engineering and technical personnel of colleges and universities have conducted research to explore the distributed optical fiber sensing system, through a variety of different research methods and means, for the development and application of distributed optical fiber sensing system laid a sufficient theoretical basis.Distributed optical fiber temperature measurement system measuring signal contains a large number of high intensity noise, signal processing is therefore essential steps in the process of measurement, is directly related to measuring accuracy and time resolution of the system. Paper deeply studied the distributed optical fiber temperature measurement system and its temperature measurement principle, and put forward the improvement methods to the signal processing system, in order to improve the performance of distributed optical fiber temperature measurement system.This paper analyses the signal processing method of distributed optical fiber temperature measurement system, using the figures at the present stage average technique can effectively improve the signal-to-noise ratio of system, but also restricts the system response time. This paper puts forward to adopt the method of wavelet threshold processing signals, using the figures mean get rid of the noise technology to improve accuracy of measurement system. In view of the wavelet decomposition method, selected the best wavelet function, determine the appropriate layer number of wavelet decomposition and choose the appropriate soft and hard threshold. Improve the measurement precision of the system, reduce system response time.