Accurately identifying internal insulation defects in gas insulated switchgear(GIS)helps assess the hazards of partial discharge(PD)and provides essential guidance for equipment maintenance.However,the traditional single monitoring method has the disadvantages of incomplete information utilization,high feature dimensionality and low recognition rate.To overcome these problems,a partial discharge experimental platform is first built on a 220 kV GIS,and four typical defect models,such as insulator fouling,insulator air gap,floating electrode and metal protrusions,are set up.The partial discharge signals of various defects are collected by pulse current method and UHF method.The fusion decision of PD signal based on time resolved partial discharge and phase resolved partial discharge is introduced into the proposed diagnosis method of BP neural network and improved DS evidence theory.By introducing the degree of evidence fusion,the proposed diagnosis algorithm is used for fault identification.The results show that the proposed recognition method can deeply mine effective information and has ability to reject false diagnosis,so that the final overall recognition rate is higher than 96.7%,which is significantly high than the traditional BP neural network.
Gas insulated switchgearpartial dischargeBP neural networkimproved DS evidence theoryfault diagnosis
NETL
NSTL
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