Study On Partial Discharge Identification And Harmfulness Assessment For Gas Insulated Switchgear

Gas insulated switchgear has inner insulation aging under the effect of voltage,heat and forces, in addition with certain hidden defects caused by producing, transportand operation, all of which will decrease the inner insulation voltage and then resultedin electric faults. Since the hidden fault appears mostly in the form of partial discharge,its accurate recognition and harmfulness assessment can give instructions for statedetection and life cycle economic management. In view of the advantage ofmulti-information fusion, the thesis constructs information fusion recognition andharmfulness assessment system of gas insulated switchgear based on DS evidencetheory, with the disregard of the traditional single sensor detection.The thesis proposes a multi sensor detection system of gas insulation switchgearwith the application of IEC60270method, fluoresense optical fiber detection and ultrahigh frequency. The experiments in a large number give three kinds of sensors detection,feature information differences and variations with partial discharge, under the defectsanalysis of metallic outshoot in gas insulation switchgear, insulator surface metalattachment, insulator inner gap and free metal particles, which demonstratecorresponding relations between three kinds of sensors and partial discharge both intheory and application.Multi information sources can give the representation of partial dischargecharacteristics in multi perspectives. According to the differences of the multiinformation characteristics, waveform feature of ultrahigh frequency time analyticsignals is extracted by wavelet decomposition in time, frequency and db4domains.Distribution feature of discharge fingerprint based on phase resolved analysis isextracted using skewness, measurable slop and other statistical parameters. Combinedwith relative information between ultrahigh frequency signals accumulation energy anddischarge amount, the partial discharge multi information fusion recognition system isconstructed based on DS evidence theory, with the demonstration of better faulttolerance, stability and recognition accuracy than independent sensor detection, underthe testing of independent defects and sample data.In the process of identification, taking the unbalanced effect from the correlation ofPRPD features into accounts, a global optimum sort criteria is designed to establish thesupport vector machine recursive feature elimination method, based on theone-versus-one Support Vector Machine classification algorithm and “Optimal Brain Damage” theory. Feature sets are optimized by principle component analysis andsupport vector machine recursive feature elimination, nine which are chosen as theoptimal feature subsets, with the demonstration of a relatively better effect for reductionof the feature dimension benefited from the SVM-RFE method.In accordance with the experiments testing and field operation experiences, threelevels of operation states are proposed and defined, namely normal, be aware of, and onalert, for the assessment of partial discharge harmfulness. With the combination of gasinsulation switchgear operation experience and relating detecting regulation, and underthe analysis of three kinds of sensors information and their correlation, the dischargeamount by IEC60270, maximal optical detection signal and variation of dischargefingerprint under partial discharge, are viewed as main principles of harmfulnessassessment. Based on the fact that the three kinds of feature information are notabsolutely positively correlated with the development degree of partial discharge, andcombined with the variation regulation of the three information in company with thestate degrees, the mapping relation between the feature information and the damagedegree of partial discharge is constructed, under the application of the fuzzymembership analysis and neural network respectively. Finally, the thesis constructs amulti information fusion assessment system of partial discharge harmfulness throughthe DS evidence theory originally.With the testing demonstration, it follows that:①All the three kinds ofindependent information can give a proper reflection on the order of severity of partialdischarge, with respect to defect models in similar structures.②The accuraterecognition of partial discharge types can give benefit to feature information calculationin detail, enhance the rationality and reliability of information mining, and improve theobjectivity and fault-tolerance of basic probability assignment settings.③Multiinformation fusion can provide a comprehensive treatment for the assessmentinformation in three kinds of methods, and can obtain better assessment performanceaccuracy and reliability, in comparison to the independent information assessments.