Study Of Detecting And Recognition Of Insulating Defect In High-voltage XLPE Power Cable Joint

Power cable line is one vital part of urban power network, and its safety, stabilityand reliability are of great significance in the urban power network. According tostatistics,110kV and above HV cable lines which have been put into operation are morethan8000kilometers in China, with the highest voltage reaching500kV.High voltage cable section is a weak point of cable line. The electric stress ofinsulation shielding fracture section in the joint and terminal can lead to a malfunctionbecause of the electric field relative concentration. With an enclosed insulation structure,joint is always assembled on the spot. Compared with the power cable and terminal, theinsulating safety margin of joint is small. In addition, high quality is required on the siteconstruction of joint. Bad construction technology or inadequate sealing measures willlead to insulation deterioration in the formidable underground operational environment.Therefore, the joint turns into the weak point vulnerable to insulation fault in the cableline.Operational experience and research indicate that: the quantity of partial dischargein cable and its insulating condition are strongly relevant. The change of the quantity ofpartial discharge indicates insulation defects which may endanger the safe operation,while the partial discharge measurement which can represent the operational conditionof high voltage cable and joint leads to the fact that the hidden fault will be discoveredin time, and significant economic and social benefits be gain because of reliableoperation of cable line. Partial discharge experiment is regarded to be the best methodfor the evaluation of XLPE cable insulating condition by experts, scholars home andabroad, and authoritative organization(such as IEC、IEEE and CIGRE).Based on the study home and abroad on partial discharge signal detection in XLPEpower cable and joint, this dissertation analyzes all detection and pattern recognitiontechnology in detail, emphasizes on partial discharge characteristic of transmission inXLPE power cable and joint, partial discharge detection technology and recognitiontechnology. The major achievements are listed as follows:①Based on the analysis of dielectric relaxation theory and dielectric property ofsemiconductive layer, modificative equation for complex dielectric constants ofsemiconductive layer which can be used in the practical calculation is obtained.According to Davidson-Cole equation, complex dielectric constants of semiconductive layer are fit and theoretical calculations while the constant substitutedinto simplified transmission line model of impedance distribution parameters weighedratio are carried out. FDTD method is adopted to carry out simulation research onpropagation characteristics of cable and joint in order to provide reference to partialdischarge detective test and failure diagnosis in joint. Four typical defect models areestablished on the basis of common insulation failure in joint.②Sensors conforming to high voltage cable and joint structure are designed.Through the establishment of circuit and antenna model, methods (such as deduction ofthe transfer function, impedance characteristic calculation, and square-wave response)are adopted to analyze frequency response characteristics and output characteristics ofsensor systematically and thoroughly, and each electrical and physical parameters ofsensor are optimized.③Partial discharge detection system composed by capacitive sensors is chosen todetect the four typical defects in110kV cable joint test platform, mass test data areobtained and partial discharge q nspectrogram and its gray image areestablished.④A method of partial discharge one-dimension feature extraction based on liftingwavelet transform is presented. In the method the dimension of lifting wavelettransform coefficients are reduced through Birge-Massart strategy, and then theeigenvector of pattern recognition are got through singular value decomposition. So lotsof gains can be got in the condition of effective feature retention, such as dimension,noise, and recognition time reduction.⑤A partial discharge feature extraction method is presented based on(2D)2MMC+LDA frame algorithm. By this method, not only the partial feature imagemining can be implemented based on the row or columns to discriminate vector, butalso the general feature can be considered based on general image. It overcomes thecrisis of dimension, eliminates singularity of within-class scatter matrix, furthest keepsstructure distribution of original sample pattern, reduces training time of sampleeffectively, improves the accuracy of pattern recognition, and implements automaticselection of parameter.