On Partial Discharge Diagnosis System Of Power Cable Based On Oscillation Wave

During the production and commissioning of cables,there may be some safety hazards that cause sudden power outages,which will bring inconvenience and losses to people’s lives and even the society’s economy.Studies have found that 80% of cable damage is caused by cable insulation defects,and partial discharge signals can reflect the insulation status of cable.Commonly used partial discharge detection methods of cable are online detection and offline detection.Among them,the oscillating wave partial discharge test system(OWTS)in offline detection has been widely used because it is equivalent to the power frequency effect and has small electrical interference.The existing oscillating wave partial discharge detection technology mainly solves the problem of localization of the local discharge source.There is relatively little research on the identification of partial discharge types,and the recognition accuracy is not high,which cannot provide favorable support for subsequent cable repair work.Therefore,in this thesis,the OWTS is deeply studied,the reason for the partial discharge of cables is simulated and analyzed.Aiming at the current lack of pattern recognition of partial discharge signals under oscillating waves,a support vector machine(SVM)based power cable partial discharge pattern recognition algorithm was proposed.The main tasks of the thesis include the following parts:First of all,the development of cable condition detection technology at home and abroad is studied.The reasons and the underlying mechanism for the partial discharge of power cables is clarified.And the working principle of the OWTS is analyzed.Aiming at the problem that the traditional classic air-gap three-capacitance model does not include the concept of induced charge,an improved air-gap partial discharge model based on system capacitance variation and the resistance and capacitance of the cable structure is proposed.The mode of oscillating wave partial discharge test system is established in SIMULINK.According to this model,the oscillating wave high voltage and partial discharge signals were obtained in accordance with the theoretical values,which proved that the model was correct.Then,for the SVM model,the regular factors and kernel function parameters have a significant impact on the classification accuracy of the algorithm,and the existing parameterseeking algorithm cannot quickly and accurately find the optimal value.An improved DEPSO optimization algorithm is proposed.The algorithm uses the advantages of particle swarm optimization(PSO)early search ability and fast convergence speed,combined with Differential Evolution(DE)algorithm has the characteristics of screening out better individuals from the group,looking for the most optimal parameters to improves the accuracy of partial discharge recognition under the oscillating wave voltage.The proposed algorithm is simulated and verificated in MATLAB.The result shows that the algorithm can quickly and efficiently find the regularization factors and kernel function parameter values,and the recognition accuracy of the SVM classifier constructed by this parameter has been improved.Finally,the types of partial discharges caused by defects in the middle joint of the cable are analyzed,and four defect models of cable joints are made according to the causes.An experimental platform for an oscillation wave partial discharge detection system is built.Aiming at the problems of complex modeling and low recognition accuracy of traditional pattern recognition,a M-ary multi-class least squares support vector machine(LS-SVM)algorithm based on improved DE-PSO is proposed to identify partial discharges under oscillating waves type to judge the cable insulation status.The algorithm collects four types of partial discharge signals,and extracts feature vectors as the input of the pattern recognition classifier according to the differences between the various types of signals.Then,based on the M-ary multi-classification idea,a partial discharge pattern recognition machine composed of a least squares support vector machine as a sub-classifier is constructed.The improved DE-PSO algorithm is applied to solve the optimal parameters of the support vector machine,and the training data is used to train the machine to obtain the optimal performance.The algorithm is verified by building a field experiment platform.The results confirm that the proposed algorithm can identify the type of partial discharge signal under the oscillating wave,judge and evaluate the cable insulation status,and provide a theoretical basis for the subsequent cable repair work.