Multi branches dilated CNN federated learning for transmission line fault diagnosis

Wenhao Sun; Hongbo Ma; Wei Li; Yangyang Pan; Fei Hao; Tao Wang

doi:10.1117/12.2611608

14 October 2021 Multi branches dilated CNN federated learning for transmission line fault diagnosis

Wenhao Sun, Hongbo Ma, Wei Li, Yangyang Pan, Fei Hao, Tao Wang

Proceedings Volume 11930, International Conference on Mechanical Engineering, Measurement Control, and Instrumentation; 119301E (2021) https://doi.org/10.1117/12.2611608
Event: International Conference on Mechanical Engineering, Measurement Control, and Instrumentation (MEMCI 2021), 2021, Guangzhou, China

Abstract

Using convolutional neural network for transmission line fault diagnosis is an accurate and effective method, but it relies on a large amount of data with positive labels and is limited by kernel size that decides the receptive field. However, it is difficult to centralize data in reality, which causes low accuracy of the model. Federated learning has made great progress recently, and it is possible to train a model with high accuracy without centralizing data. In this paper, we propose a transmission line fault diagnosis method based on multi-branch convolutional neural network combined with federated learning. First, we design a novel three branches network with two dilated convolution kernels to increase the receptive field of the kernel. Then we integrate it into the federated learning framework to expand the amount of data used to train the model while preserving data security and privacy. The experimental results show that our method is feasible and can effectively improve the accuracy of the model, and provides a new idea for transmission line fault diagnosis.

Citation Download Citation

Wenhao Sun, Hongbo Ma, Wei Li, Yangyang Pan, Fei Hao, and Tao Wang "Multi branches dilated CNN federated learning for transmission line fault diagnosis", Proc. SPIE 11930, International Conference on Mechanical Engineering, Measurement Control, and Instrumentation, 119301E (14 October 2021); https://doi.org/10.1117/12.2611608

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
8 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Data modeling

Convolution

Convolutional neural networks

Artificial intelligence

Computer security

Neural networks

Databases

Show All Keywords

Keywords/Phrases

Search In:

Publication Years