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Research On On-line Monitoring And Fault Diagnosis System Of GIS Insulation

Posted on:2016-05-22Degree:MasterType:Thesis
Country:ChinaCandidate:G S ChengFull Text:PDF
GTID:2272330467483502Subject:High Voltage and Insulation Technology
Abstract/Summary:PDF Full Text Request
GIS devices are widely used in city power supply, coal mining, metallurgy and otherfields, once the failure will bring huge economic loss, to ensure GIS safety trouble-freeoperation is great significant. For the working GIS device, the partial discharge signalgenerated by insulation defects can be collected by UHF sensor. Then uploading data to themonitoring and diagnosis system to implement the online monitoring and fault diagnosis.Based on the analysis of partial discharge mechanism, this paper analyses the propagationcharacteristics and attenuation characteristics of ultrahigh frequency electromagnetic wavewithin the GIS, and simulates the propagation characteristics and attenuation characteristics ofpartial discharge signal using the electromagnetic simulation software XFDTD.To receive the UHF partial discharge signal, the plane Archimedean spiral antenna isdesigned. According to the partial discharge UHF band parameters, the author designsArchimedean spiral antenna geometry parameters, balun structure. Simulated by3Delectromagnetic simulation software ANSOFT HFSS, results confirm that the antenna systemis reasonable and meet the design requirements.In fault diagnosis system, obtaining the original signal without interference is particularlyimportant, but the site signal acquisition will inevitably introduce all kinds of interference. Thehardware filter circuit can eliminate most of the interference, but the white noise cannot beeliminated from the partial discharge signal. Wavelet analysis has the characteristic ofmulti-resolution in time domain and frequency domain. Wavelet soft threshold filteringalgorithm is effective in dealing with the white noise.Insulation faults occupy the main part in all GIS faults, this paper aims at five kinds ofmain insulation defect and extracts time domain characteristic parameters of the partialdischarge pulse signal, which generate in the insulation defect. Using the sample data to trainthe BP neural network, to achieve discharge type recognition, fault prediction and arrangemaintenance plan, to ensure the safe operation of GIS.
Keywords/Search Tags:GIS, Archimedean spiral antenna, Wavelet analysis, Characteristic parameters, BP neural network
PDF Full Text Request
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