Detection of gas decomposition products is widely used for condition diagnosis of SF6-insulated equipment because of its an- ti-electromagnetic-interference ability and high sensitivity. Previous investigations show t...Detection of gas decomposition products is widely used for condition diagnosis of SF6-insulated equipment because of its an- ti-electromagnetic-interference ability and high sensitivity. Previous investigations show that the volume of gas chamber influences the types and concentrations of SF6 decomposition products. Therefore using a newly developed dual gas chromatography (GC) detection sys- tem we investigated the discharge and decomposition of SF6 in a discharge chamber with its volume close to that of the real chambers in GIS. Tests in the chamber were performed with different applied voltage, different electrode arrangements, and different defect types. For discharge between needle-to-plane electrodes, the typical gas decomposition products are SO2F2, SO2 and S2OF10. A near linear growth with the increase of voltage duration is found in the concentration of SO2F2, whereas the growth rates of SO2 and S2OF10 concentration decrease with time. Concentrations of SO2F2, SO2 and S2OF10 at the same voltage duration decrease with the decrease of the voltage amplitude and the increase of the needle-to-plane distance. Change of the gas chamber volume affects the generation rates of SO2F2 and SO2, however not S2OF10. For insulator surface defects, the typical gas decomposition products are CF4, CS2 and SO2. Among which, the concentrations of CF4 and SO2 increase with the voltage duration almost linearly. Moreover, a new parameter that represents the degree of SF6 degradation, the SF6 deterioration ratio, is proposed. In the needle-to-plane case, SF6 deterioration ratio is positively correlated to the fitting value of an averaged discharge capacity. However, the maximum value of SF6 deterioration ratio varies with the defect type.展开更多
This paper proposes a deep-learning-based Robin-Robin domain decomposition method(DeepDDM)for Helmholtz equations.We first present the plane wave activation-based neural network(PWNN),which is more efficient for solvi...This paper proposes a deep-learning-based Robin-Robin domain decomposition method(DeepDDM)for Helmholtz equations.We first present the plane wave activation-based neural network(PWNN),which is more efficient for solving Helmholtz equations with constant coefficients and wavenumber k than finite difference methods(FDM).On this basis,we use PWNN to discretize the subproblems divided by domain decomposition methods(DDM),which is the main idea of DeepDDM.This paper will investigate the number of iterations of using DeepDDM for continuous and discontinuous Helmholtz equations.The results demonstrate that:DeepDDM exhibits behaviors consistent with conventional robust FDM-based domain decomposition method(FDM-DDM)under the same Robin parameters,i.e.,the number of iterations by DeepDDM is almost the same as that of FDM-DDM.By choosing suitable Robin parameters on different subdomains,the convergence rate is almost constant with the rise of wavenumber in both continuous and discontinuous cases.The performance of DeepDDM on Helmholtz equations may provide new insights for improving the PDE solver by deep learning.展开更多
基金Project supported by International Cooperation Project in Shaanxi Province of China (2012KW-01)
文摘Detection of gas decomposition products is widely used for condition diagnosis of SF6-insulated equipment because of its an- ti-electromagnetic-interference ability and high sensitivity. Previous investigations show that the volume of gas chamber influences the types and concentrations of SF6 decomposition products. Therefore using a newly developed dual gas chromatography (GC) detection sys- tem we investigated the discharge and decomposition of SF6 in a discharge chamber with its volume close to that of the real chambers in GIS. Tests in the chamber were performed with different applied voltage, different electrode arrangements, and different defect types. For discharge between needle-to-plane electrodes, the typical gas decomposition products are SO2F2, SO2 and S2OF10. A near linear growth with the increase of voltage duration is found in the concentration of SO2F2, whereas the growth rates of SO2 and S2OF10 concentration decrease with time. Concentrations of SO2F2, SO2 and S2OF10 at the same voltage duration decrease with the decrease of the voltage amplitude and the increase of the needle-to-plane distance. Change of the gas chamber volume affects the generation rates of SO2F2 and SO2, however not S2OF10. For insulator surface defects, the typical gas decomposition products are CF4, CS2 and SO2. Among which, the concentrations of CF4 and SO2 increase with the voltage duration almost linearly. Moreover, a new parameter that represents the degree of SF6 degradation, the SF6 deterioration ratio, is proposed. In the needle-to-plane case, SF6 deterioration ratio is positively correlated to the fitting value of an averaged discharge capacity. However, the maximum value of SF6 deterioration ratio varies with the defect type.
基金National Key R&D Program of China Nos.2019YFA0709600,2019YFA0709602China NSF under the grant numbers Nos.11831016,12171468,11771440,12071069+1 种基金the Fundamental Research Funds for the Central Universities(No.JGPY202101)the Innovation Foundation of Qian Xuesen Laboratory of Space Technology。
文摘This paper proposes a deep-learning-based Robin-Robin domain decomposition method(DeepDDM)for Helmholtz equations.We first present the plane wave activation-based neural network(PWNN),which is more efficient for solving Helmholtz equations with constant coefficients and wavenumber k than finite difference methods(FDM).On this basis,we use PWNN to discretize the subproblems divided by domain decomposition methods(DDM),which is the main idea of DeepDDM.This paper will investigate the number of iterations of using DeepDDM for continuous and discontinuous Helmholtz equations.The results demonstrate that:DeepDDM exhibits behaviors consistent with conventional robust FDM-based domain decomposition method(FDM-DDM)under the same Robin parameters,i.e.,the number of iterations by DeepDDM is almost the same as that of FDM-DDM.By choosing suitable Robin parameters on different subdomains,the convergence rate is almost constant with the rise of wavenumber in both continuous and discontinuous cases.The performance of DeepDDM on Helmholtz equations may provide new insights for improving the PDE solver by deep learning.
