An automated computation method for flexible protection systems based on neural networks

Abstract

An automated computation method is proposed in this paper to address the problems of enormous computational difficulty and low efficiency in flexible protection systems. This method uses neural networks to demonstrate the complex nonlinear mapping relationships between the system’s specifications and performance. Use the finite element numerical simulation model validated by a full-scale impact test on the 750 kJ passive flexible protection system for massive parametric analysis, and extract the critical computation results as the label space. Conducted mechanical tests on critical components, analyzed the mechanical characteristics of each component, and extracted training features to form a sample space. The label space and sample space formed a dataset containing 1828 sets of data and used 1700 sets of data to train a neural network while using the remaining 128 sets of data to test. The maximum predicted error was 8.33 %, and the maximum average error was 3.73 % from the test. The proposed method can complete the computation in just 6 s, with a computation speed of 520 times that of the fastest finite element method, significantly improving the computation efficiency of the flexible protection system and reducing the computational difficulty.