Review of Charge Accumulation on Spacers of Gas Insulated Equipment at DC Stress

Charge accumulation on the spacer surface in gas insulated equipment is very severe in the DC field,leading to easy flashover,which restricts the application of DC GIS/GIL.Therefore,knowledge about the charge accumulation characteristics of gas insulated equipment at DC stress is essential.In this paper,we reviewed the research methods and the characteristics of charge accumulation on spacers.A summary of charge measurement methods and setups are presented.And the surface charge inversion algorithms are introduced.Then the simulation model for charge accumulation in the DC field is reviewed.Subsequently,the charge accumulation mechanisms and phenomenon,the influence factor of surface charge accumulation and the influence of accumulated charge on spacer insulation characteristics are summarized.In addition,some suppression methods of surface charge accumulation are discussed.Finally,based on the understanding of charge accumulation on spacers,some suggestions on further studies are presented to aid in the design of a better spacer free from surface charges.

THz Wave Detection of Gap Defects Based on a Convolutional Neural Network Improved by a Residual Shrinkage Network

Internal air gap is a serious type of defect in the insulation equipment,which threatens the safe operation of the power grid.In order to diagnose the position and thickness of the internal air gap,this paper proposes a terahertz wave detection method based on wavelet analysis and a CNN(convolution neural network)model.According to the time-frequency characteristics of the wavelet cluster,the calculation method of air gap depth is proposed.To determine the thickness of the internal air gap,the performances of several classification methods,such as waveform feature analysis,Bayes,MLP(Multi-layer Perceptron),SVM(Support Vector Machine)and CNN are compared.The results show that the CNN modified by a residual shrinkage network and SVM(CNN-RSN-SVM)has the best performance.By adjusting the parameters,the classification accuracy of the CNN-RSN-SVM model can be elevated to 98.91%.Furthermore,the 3D imaging method of air gap defect based on wavelet analysis and CNNRSN-SVM classification model is formed.