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

Yong Jiang; Yang Feng; Xiuyan Lai; Honghua Huang; Daojun Yu; Yajun Wu

doi:10.1117/12.3025428

28 February 2024 Dynamic simulation analysis of high-voltage circuit breaker switch spring mechanism based on deep learning

Yong Jiang, Yang Feng, Xiuyan Lai, Honghua Huang, Daojun Yu, Yajun Wu

Author Affiliations +

Proceedings Volume 13071, International Conference on Mechatronic Engineering and Artificial Intelligence (MEAI 2023); 130713R (2024) https://doi.org/10.1117/12.3025428
Event: International Conference on Mechatronic Engineering and Artificial Intelligence (MEAI 2023), 2023, Shenyang, China

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.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yong Jiang, Yang Feng, Xiuyan Lai, Honghua Huang, Daojun Yu, and Yajun Wu "Dynamic simulation analysis of high-voltage circuit breaker switch spring mechanism based on deep learning", Proc. SPIE 13071, International Conference on Mechatronic Engineering and Artificial Intelligence (MEAI 2023), 130713R (28 February 2024); https://doi.org/10.1117/12.3025428

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
6 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Circuit switching

Deep learning

Device simulation

Education and training

Mathematical modeling

Data modeling

Spindles

Show All Keywords

Keywords/Phrases

Search In:

Publication Years