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.

中文翻译：

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基于图像识别和深度学习的高压断路器喷嘴参数优化研究

用于高压设备的传统建模和优化方法，特别是断路器组件和喷嘴，需要人工测试和大量参数的改进，并且效率相对较低。在这种情况下，人工智能技术在复杂系统的识别和预测中强大的处理能力是一种有效的解决方案。本文提出了结合断路器喷嘴深度学习的图像识别优化方法。使用商业软件（COMSOL Multiphysics 5.4）中的高马赫流模型进行喷嘴模型研究，研究冷流期间的气体流动状态和行为，并基于卷积神经网络获得喷嘴腔的冲击波图像识别模型（ CNN）多尺度分层特征的方法。在有效图像识别的基础上，结合深度学习卷积递归神经网络（CRNN），将不同参数下的图像序列发送到卷积层进行特征提取，然后将特征图输入到循环中。通过该层获得预测序列，最后通过预测层预测喷嘴的运动学参数与内部气体流动状态之间的关系。结果表明，根据CRNN的预测，喉长范围应在7到13 mm之间，角度应在8到15°之间。所提出的方法还可以用于相似的材料和组件，具有一定的通用性。©2021日本电气工程师学会。由Wiley Periodicals LLC发布。