| The existing partial discharge detecting methods for gas insulated switchgear (GIS)have some kinds of disadvantages. For pulse current method, ultrasonic method, andoptic detecting method, the problems of anti-jamming capability, detection accuracy,and costing make them not suitable for accurate on-line partial discharge monitoring.Though ultrahigh frequency method has lots of advantages, its pattern recognitioncapability has a far way to meet the industry requirement. Also discharge capabilitycalibration has not been solved yet for ultrasonic method, optic detecting method, andultrahigh frequency method. Therefore, the partial discharge detecting methods usednow can not completely meet the requirement of on-ling partial discharge monitoring.Research shows that partial discharge can decompose SF6. The decomposedproducts continue to react with moisture and oxygen in GIS and lots of chemicals aregenerated. Therefore, partial discharge in GIS can be discovered by detecting SF6decomposition products. As the SF6decomposition products generated by differentkinds of partial discharge are different in concentration, generation rate, andconcentration ratio, it is also possible to recognize partial discharge category by SF6decomposition products. It is essentially a kind of chemical method to detect partialdischarge by SF6decomposition products, which has the capability to resist the strongelectromagnetic and noise interference in field. So this method is significant in boththeory and application. But lots of problems have not been solved yet for this method,such as which decomposition product should be chose as the feature decompositionproduct, how to use the decomposition product to recognize the partial dischargecategory, severity and development trend, and what is the specific criteria.Based on stability, how easy to detect, and physical significance, four kinds of SF6decomposition products, namely, SO2F2、SOF2、CF4and CO2, were chose as thefeature decomposition products to analyze the SF6decomposition characteristic underdifferent kinds of partial discharge. The SF6decomposition experiments were conductedon the SF6decomposition device under four kinds of partial discharge including metalprotrusion, free conductive particle, contamination on insulator surface, and gap oninsulator-conductor interface. The concentration of the four kinds of featuredecomposition products were measured under each experiment. The characteristic of thefeature decomposition products concentration and feature concentration ratio was analyzed. Also the decomposition characteristics of SF6under each kind of partialdischarge were compared. It is found that the decomposition products are obviouslydifferent in concentration, generation rate, and concentration ratio under the four kindsof partial discharge. It is feasible to use SF6decomposition products to recognize thepartial discharge category.The concentration data of the feature decomposition products from the four kindsof partial discharge were clustered be fuzzy C-means clustering. And the concentrationand concentration ratio were used as the feature parameter respectively. The comparisonof the results shows that the performance using the concentration ratio as the featureparameter is much better than that of the concentration. So the concentration ratio waschose as the feature parameter for partial discharge recognition in this paper. Threeconcentration ratios were employed, namely, c(SO2F2)/c(SOF2), c(CF4)/c(CO2), andc(CF4+CO2)/c(SO2F2+SOF2). The physical significance of the three concentration ratioswas also analyzed. A decision tree was established for partial discharge recognition bydecision tree theory, using the three concentration ratios as the feature parameters. Thetest result shows that the decision tree has a good performance on partial dischargerecognition. Meanwhile, a smart recognition system was established for partialdischarge recognition, based on support vector machine theory. The parameters of thesupport vector machine were optimized by particle swarm optimization theory. The testresult shows that this smart system has a better performance than that of decision tree.The SF6decomposition experiments were conducted under different moisture andoxygen contents. Partial discharge was generated by the protrusion defect. And theconcentration of the four feature decomposition products was measured in eachexperiment. Through analysis of the data, it is found that the concentration of SOF2,SO2F2, and CO2increases with the initial content of moisture and oxygen. And theconcentration of CF4barely changes with the content of moisture and oxygen. After that,the characteristic of the three concentration ratios was also analyzed. It is shown that thevalue of the three concentration ratios all decreases with the content of moisture andoxygen. The content of moisture and oxygen has a strong influence on the partialdischarge recognition system. The recognition accuracy decreases dramatically whenthe content of moisture and oxygen changes.The chemical reactions generating the four feature decomposition products are allsecond order reactions by chemical kinetics theory. Based on the mathematical relationof reactant and resultant, it is found that the relation of the three concentration ratios and the initial content of moisture and oxygen in the gas chamber can be expressed bypower function. Then the mathematical model of influence rule of moisture and oxygencontent on the feature concentration ratios was developed by least square fitting methodusing the concentration data of feature concentration ratios under different moisture andoxygen content. According to the mathematical model, the calibration method ofinfluence of moisture and oxygen on the feature concentration ratios was proposed,which can calibrate the value of the feature concentration ratios from one moisture andoxygen content to the other content. The recognition accuracy before calibration wascompared with that of after calibration using the partial discharge recognition systembased on support vector machine. The result shows that the recognition accuracyincreases significantly when the input data are calibrated by the calibration method,which means the calibration method has a good performance. |
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