Study On Detecting Method Of Multicomponent Gases Based On Photoacoustic Spectrum Technology

Oilpaper is the main insulation of oil-filled power transformer. It will decompose and bring dissolved gases in oil in long time operation of transformer because of overheat and partial discharge. Detecting of the concentration and component of dissolved gases in oil can judge faults condition effectively .So detecting of the concentration and component of dissolved gases in transformer oil is of great significance to the operation safety of transformer. There are two primary problems existing in the detecting devices of dissolved gases in transformer oil: one is that the precision and long-time stability of sensor is not high, and the other is that cross influence of multicomponent gases bring detecting error. To solve these two problems, we need to choose new-style gas-detecting technology.The photoacoustic spectrum technology is a minim gas detecting technology. It has many advantages and can be applied in detecting of multicomponent gases dissolved in transformer oil well. Considering the current research status of detecting technology of gas photoacoustic spectrum and aiming at its disadvantages, this paper starts with application of photoacoustic spectrum in detecting of multicomponent gases. Based on the bringing mechanism of photoacoustic signal, the relation character between signal value and concentration of other characteristic gases is analyzed. According to the principle of photoacoustic spectral detecting, this paper represents composing components of test platform used to detect dissolved gases in oil ,and design photoacoustic spectral test platform based on the requirement of application on operation scene of transformer ,then analyzes the principle and design realization of the pivotal component-photoacoustic cell.Using LabVIEW assorted with Simulink simulation model to set up virtual signal detection system as a compositive part of photoacoustic spectral test platform and aiming at the strong noise interference in detection process of photoacoustic signal, this paper combines cross-correlation analysis with chaotic detection to pick up weak signal. The experimental results of detecting photoacoustic signal represents this virtual system is very sensitive to weak signal under strong noise and has a high detecting precision, it can restrain strong noise in the detecting process effectively and improve signal-noise ratio.According to the contrast of experimental results between single-component gas and multicomponent gases with photoacoustic spectral test platform, this paper analyzes the absorption cross influence among multicomponent gases and sets up revision model of cross influence based on radial basis neural network in MATLAB, and ameliorates its modeling arithmetic to improve the performance of network's. The experimental results represents this method can revise the cross influence among multicomponent gases effectively and predigest detecting steps of gas photoacoustic spectrum because of self-adaptation of network's.