Identification of faulty station area line loss based on the fusion of cross attention and deep learning algorithm

HongXia Zhu; SongHui Zhang; CongCong Li; Qing Wang; PingXin Wang; Chao Yu

doi:10.1117/12.2615264

15 October 2021 Identification of faulty station area line loss based on the fusion of cross attention and deep learning algorithm

HongXia Zhu, SongHui Zhang, CongCong Li, Qing Wang, PingXin Wang, Chao Yu

Author Affiliations +

Proceedings Volume 11933, 2021 International Conference on Neural Networks, Information and Communication Engineering; 119331D (2021) https://doi.org/10.1117/12.2615264
Event: 2021 International Conference on Neural Networks, Information and Communication Engineering, 2021, Qingdao, China

Abstract

A Convolutional Neural Network (CNN) for theoretical station area line loss is proposed in this paper. Considering that CNN has strong nonlinear fitting ability, it is often used to predict the station area line loss. We analyze case, and select appropriate number of input features to verify proposed method’s availability. Meanwhile, the station area line loss is calculated under the most appropriate number of feature inputs. The results show that the station area classification and key factors are identified as the subsequent station area loss calculation model, which optimizes the input variables and improves efficiency and accuracy for station area line loss calculation.

Citation Download Citation

HongXia Zhu, SongHui Zhang, CongCong Li, Qing Wang, PingXin Wang, and Chao Yu "Identification of faulty station area line loss based on the fusion of cross attention and deep learning algorithm", Proc. SPIE 11933, 2021 International Conference on Neural Networks, Information and Communication Engineering, 119331D (15 October 2021); https://doi.org/10.1117/12.2615264

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