| Power transformer is the hub equipment connecting transmission and distribution among regional power grids.Its stability directly affects the reliable operation of the power grid.Once failure occurs,it will cause a large area of power failure,transformer explosion and other malignant accidents,which will cause huge losses to the national economy and have a serious social impact.Therefore,it is of great significance to carry on the health monitoring to the running state of transformer.At present,gas chromatography,photoacoustic spectroscopy and other methods are often used to detect the concentration of dissolved gas in transformer insulating oil,so as to realize the monitoring of transformer running state,but there are often problems such as poor reproducibility,large sampling randomness and incomplete detection parameters.In recent years,gas concentration detection spectroscopy based on Tunable Laser Absorption Spectroscopy（TDLAS）has high selectivity and sensitivity,which has attracted wide attention from scholars.However,due to the characteristics of dissolved gas in insulating oil（transient,trace and difficult to preserve,etc.）and the particularity of the surrounding environment of power transformer（outdoor,strong electromagnetic interference）,there are still many problems to be solved in the online detection technology of dissolved gas concentration in transformer insulating oil based on TDLAS.On the basis of fully studying the characteristics and extraction rules of dissolved gas in transformer insulating oil,this paper has carried out the development of an online detection device for dissolved gas concentration in transformer insulating oil based on TDLAS,considering such factors as online detection,free sampling,cost and environmental application.The research results have important economic and practical significance for ensuring the safety and reliable operation of power system.The main research contents of this paper are as follows:（1）Aiming at the problem that the slow separation speed of traditional oil and gas affects the on-line detection efficiency of trace gas,the on-line headspace oil and gas separation method was studied.Firstly,the pyrolysis mechanism of transformer insulating oil was studied,and the decomposition products of transformer insulating oil were analyzed statistically by thermodynamic model.Secondly,the diffusion characteristics of characteristic gas in insulating oil were studied,and the slope of the relationship between molecular mean azimuth-shift and time was used to characterize the strength of gas diffusion,and the diffusion trajectory of characteristic gas in insulating oil was studied.Finally,a multi-parameter dynamic headspace equilibrium model was constructed to study the mechanism of the influence of temperature,pressure,stirring rate and oil sample volume ratio on oil and gas separation,and the corresponding oil and gas separation structure was designed to ensure the stability and improve the degassing speed.（2）Focusing on the problem that the traditional dissolved gas detection system in transformer oil cannot meet the fast and accurate online detection requirements of multi-component trace gas,a 6-component gas online detection system based on near-infrared TDLAS is developed.Firstly,in order to ensure the effectiveness of online detection,an integrated oil and gas separation system was designed based on the low pressure,constant temperature and dynamic headspace balance method,and the degassing efficiency was verified by experiments.The improved oil mist filter was used to minimize the influence of oil mist on laser light intensity.Secondly,by studying the near infrared absorption characteristics of methane（CH4）,ethane（C2H6）,ethylene（C2H4）,acetylene（C2H2）,carbon monoxide（CO）and carbon dioxide（CO2）,A multi-wavelength Distributed Feedback Laser（DFB）emitting system for 6-component gas detection was constructed.Finally,in view of the ultra-long optical path requirements for trace gas detection,an ultra-long optical path cell based on white pool is designed and optimized.（3）In order to solve the problem of low accuracy of online detection of multi-component gas concentration by TDLAS technology,a calculation method of multi-component gas concentration based on multi-characteristic parameters was proposed.According to the second harmonic signal of gas in transformer oil measured in practice,the multi-characteristic parameters（left peak-valley value feature,right peak-valley value feature and spacing）are extracted.Finally,multiple linear regression model is used to invert the final multi-component gas concentration values by using the extracted multi-characteristic parameters.The experimental results show that the accuracy of the second harmonic concentration calculated by using the multi-characteristic parameters is significantly improved compared with the traditional method.（4）The test of dissolved gas in transformer insulating oil was carried out and the performance of the test device was evaluated.Firstly,the oil and gas separation experiment was carried out to evaluate the oil and gas separation time.The results showed that the degassing system developed in this paper could complete the target gas degassing within 1 hour.Secondly,a performance comparison experiment was carried out between the detection system and the gas chromatograph.The experimental results show that the deviation of the detection system and the gas chromatograph in the measurement of the 6 components of gas concentrations are as follows:CO 1.67μL/L,CO2-13.47μL/L,CH40.05μL/L,C2H61.96μL/L,C2H43.16μL/L,C2H20.09μL/L.On this basis,the system sensitivity measurement experiment is carried out,and the Allan variance is used to detect the system sensitivity.The detection sensitivity of the system was 27 n L/L for acetylene,105 n L/L for methane,411 n L/L for ethylene,683 n L/L for ethane,5.85μL/L for carbon monoxide and8.55μL/L for carbon dioxide.Finally,a third-party testing experiment was carried out to verify that the online detection device of dissolved gas concentration in oil developed in this paper reached the expected index through repeated detection of oil samples with known concentration. |
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