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Finite element analysis is carried out for cylindrical shells with large openings subjected to axial tension. By utilizing a parametric study, the paper investigates the effect of the opening, on the stresses in cylindrical shells. It has been found that any increase in (a/R) ratio has produced a correspondent increase in the stress concentration factor. This is particularly true for higher values of curvature parameter. Comparing with analytical solutions are also made and the overall agreement between the two sets of results is reasonably good.
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This paper presents a methodology for estimating the normalized transient stability margin by using the multilayered perceptron (MLP) neural network. The complex relationship between the input variables and output variables is established by using the neural networks. The nonlinear mapping relation between the normalized transient stability margin and the operating conditions of the power system is established by using the MLP neural network. To obtain the training set of the neural network the potential energy boundary surface (PEBS) method along with time domain simulation method is used. The proposed method is applied on IEEE 9 bus system and the results shows that the proposed method provides fast and accurate tool to assess online transient stability.
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Finite element analysis is carried out for cylindrical shells with large openings subjected to axial tension. By utilizing a parametric study, the paper investigates the effect of the opening, on the stresses in cylindrical shells. It has been found that any increase in (a/R) ratio has produced a correspondent increase in the stress concentration factor. This is particularly true for higher values of curvature parameter. Comparing with analytical solutions are also made and the overall agreement between the two sets of results is reasonably good.
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This paper presents a methodology for estimating the normalized transient stability margin by using the multilayered perceptron (MLP) neural network. The complex relationship between the input variables and output variables is established by using the neural networks. The nonlinear mapping relation between the normalized transient stability margin and the operating conditions of the power system is established by using the MLP neural network. To obtain the training set of the neural network the potential energy boundary surface (PEBS) method along with time domain simulation method is used. The proposed method is applied on IEEE 9 bus system and the results shows that the proposed method provides fast and accurate tool to assess online transient stability.
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