Dynamic simulation analysis of high-voltage circuit breaker switch spring mechanism based on deep learning

Abstract

As a key component of circuit breaker switch, the dynamic characteristics of spring mechanism are directly related to the response speed and stability of high voltage circuit breaker switch. Therefore, based on deep learning technology, this paper makes a dynamic simulation analysis of the switch spring mechanism of high voltage circuit breaker. Firstly, the operational data is decomposed into multiple IMF modal components using Empirical Mode Decomposition (IMF) method, and correlation coefficients are calculated to extract features. Secondly, using the Long Short Term Memory Network (LSTM) model, the feature inputs are learned and trained, and the dynamic mathematical model results of the spring mechanism are output. The forget gate and input gate in the model respectively handle information retention and update, achieving accurate simulation of dynamic simulation. The update of weights adopts the adaptive momentum estimation gradient optimization algorithm, which improves the accuracy of the model. By comparing the simulation results of traditional methods and deep learning methods, the superiority of deep learning algorithms in the dynamic simulation of high-voltage circuit breaker switch spring mechanisms was verified. By comparing the simulation results of traditional methods and deep learning methods, the superiority of deep learning algorithms in the dynamic simulation of high-voltage circuit breaker switch spring mechanisms was verified.