Inversion of cable internal temperature based on microwave radiation measurement and GA-BP neural network

PANG Kai; FENG Li; ZHENG Wenchao

doi:10.16186/j.cnki.1673-9787.2022050029

Abstract：Objectives The current cable monitoring system could not measure the cable internal tempera-ture non-destructively.Methods Therefore,based on microwave radiation measurement,a cable internal tem-perature inversion method was proposed.Firstly,the multilayer microwave radiation transmission model was constructed by using the incoherent approach,and the exponential function was used for layering.The bright temperature data set measured by microwave radiometer was obtained through finite element simulation.The 10-fold cross-validation was applied to divide the data set.To simulate the effect of the environmental noise on microwave radiometer measured brightness temperature in actual measurement,Gaussian noise with SNR ranging from 10～50 dB was added to the calculated value as the measured value for the microwave radiom-eter.After that,the GA-BP neural network model was constructed,and then the network was trained by using the data set.Finally,the inversion performance of the pre-trained GA-BP neural network model was verified by using the measured values.Results The specific conclusions were as follows:Compared with the one that used the BP neural network only,the optimized BP one adopting a genetic algorithm was more effective in the cable internal temperature inversion.The inversion effect of the network and the signal-to-noise ratio(SNR)of added noise were positively correlated.The larger the SNR,the better the inversion effect.When the SNR was greater than 36 dB,the coefficient of determination was greater than 0.627,and the root mean square error was less than 5.55,which indicated that the inversion performed well.The inversion perfor-mance probably attained best when the SNR was 50 dB.The coefficient of determination could reach 0.985.Conclusions The results proved that GA-BP neural network was effective for cable internal temperature in-version,lending a new perspective for cable internal temperature inversion.