Research on Parameters Optimization of High Voltage Circuit Breaker Nozzle Based on Image Recognition and Deep Learning

Abstract

Traditional modeling and optimization methods for high voltage equipment, specially, circuit breaker components and nozzles, require manual testing and improvement of a large number of parameters, and the efficiency is relatively low. The strong processing power of artificial intelligence technology in the identification and prediction of complex systems is an effective solution in such case. This paper presents the optimization approach of image recognition combined with deep learning for circuit breaker nozzle. The nozzle model was conducted using the high Mach flow model in a commercial software (COMSOL Multiphysics 5.4) to study the gas flow state and behavior during cold flow, and obtains Shock wave image recognition model of the nozzle chamber based on the convolutional neural network (CNN) method of multiscale layered features. On the basis of effective image recognition, combined with deep learning Convolutional Recurrent Neural Network (CRNN), the image sequence under different parameters is sent to the convolutional layer for feature extraction, and then the feature map is input into the loop. The prediction sequence is obtained through the layer, and finally the relationship between the kinematic parameters of the nozzle and the internal gas flow state is predicted through the prediction layer. Results indicated, according to the prediction of CRNN, the range of the throat length should be between 7 and 13 mm and the angle should be between 8 ∼ 15°. The presented method could be also used for similar materials and components, with certain universality. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.