Study On Metro Stray Current Distributing And Corrosion Intelligent Monitoring Method

Nowadays Urban rail transit is in large-scale construction in our country. As metrostray current distribution and stray current corrosion monitoring is a key subject in theconstruction and operation of urban rail transit, this dissertation researches on metrostray current distribution and stray current corrosion monitoring which is importanttheoretically and practically for healthy development of urban rail transit.The content of this dissertation is presented as following. Stray current distributionmodeling in two conditions:1) different operation of single metro locomotive,2)simultaneous running of multi-metro locomotives; the coupling constraint definition tothe stray current of reflux systems parameters; the corrosion principle and the detectingmethods of stray current studying, as well as corrosion abnormal signal of stray currentbased on time-frequency joint analyzing, meanwhile, stray current corrosion degree byRadial Basis Function(RBF) predicting, and intelligent monitoring system of straycurrent corrosion based on WEB application.Based on stray current distribution static model of single metro locomotive,firstlythis dissertation built stray current distribution dynamic analytical model in differentoperation conditions of single metro locomotive, taking the locomotive running positionas the cut-off point, we divided the supply sector into different analysis domaininterlocking at the cut-off point under complete boundary condition, and computed themetro locomotive traction after determined relationship between locomotive takingcurrent and the backflow values. Then based on analytical model we simulated straycurrent distribution under single metro locomotive. Research results shows that thedynamic analytical model is consistent with actual measured value..Secondly this dissertation established finite element model of stray currentdistribution in simultaneous running of multi-metro locomotives. Based on symmetryand positive definiteness of admittance matrix, incomplete Joe Arguelles conjugategradient algorithm was put forward, which improves convergence rate of the model.Conducting constant power iteration of the locomotive equivalent conductance to satisfymatching requirements between the locomotive running power and traction substationoutput, stray current distribution was studied in different operatingconditions:1)simultaneous traction state and braking state of double metrolocomotive,2)the traction and braking state of single locomotive,3)presence oflocomotive taking current outside traction substation. Thirdly this dissertation researched metro stray current corrosion principle anddetecting methods, and proposed stationary test of stray current corrosion signal; Basedon short-time Fourier transformation, continuous wavelet transformation and Stransformation, time and frequency components of the stray current corrosion signalwithout noise was extracted, simultaneously time-frequency joint analysis of straycurrent corrosion abnormal signal was completed. Research results show that frequencyand time resolution of S transformation are superior to other two methods, timeresolution of S transformation accurately distinguishes the time and frequencycomponents of abnormal signal from noise using S transformation.Next this dissertation proposed a stray current corrosion model to predictpolarization potential offset value of buried metal based on RBF neural network, as theexperimental results show polarization potential offset value of buried metal is effectedby the following parameters: horizontal distance from buried metal to the rail, depth, thesurrounding soil resistivity, positive and negative pole spacing of power supply andsupply voltage. Taking simulation experiment data of the stray current corrosion as theinput/output samples in the prediction model, we gathered input/output samples based onrival penalized competitive learning method, taking the number of clustering asconcealed layer node number in the predictive model, combining deficiency existed inthe traditional RBF neural network structure parameter study, and then putt forwardimproved particle swarm optimization and adaptive genetic algorithm to implementoptimization of predictive network structure parameters, finally performance evaluationsystem of corrosion prediction model is established. The prediction results show thatRBF neural network optimized by improved particle swarm optimization in convergenceaccuracy and predictive performance is superior to the adaptive genetic algorithm andtraditional RBF neural network, which is effective in the prediction of the stray currentcorrosion.From above, this dissertation constructed the intelligent monitoring system of straycurrent corrosion based on WEB and introduced application of the system in metro.There are74figures,28tables,201references in the dissertation.