Detection of Foreign Objects Intrusion Into Transmission Lines Using Diverse Generation Model

Wu, Yuyao; Zhao, Shuanfeng; Xing, Zhizhong; Wei, Zheng Jie; Li, Yang; Li, Yao

doi:10.1109/TPWRD.2023.3279891

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Detection of Foreign Objects Intrusion Into Transmission Lines Using Diverse Generation Model

Author(s): Yuyao Wu ¹ , Shuanfeng Zhao ¹ , Zhizhong Xing ² , Zheng Wei ¹ , Yang Li ¹ , Yao Li ¹

Publication date (Print): October 2023

Journal: IEEE Transactions on Power Delivery

Publisher: Institute of Electrical and Electronics Engineers (IEEE)

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Shuanfeng Zhao: (View ORCID Profile)

Zhizhong Xing: (View ORCID Profile)

Journal

Title: IEEE Transactions on Power Delivery

Abbreviated Title: IEEE Trans. Power Delivery

Publisher: Institute of Electrical and Electronics Engineers (IEEE)

ISSN (Print): 0885-8977

ISSN (Electronic): 1937-4208

Publication date Created: October 2023

Publication date (Print): October 2023

Volume: 38

Issue: 5

Pages: 3551-3560

Affiliations

[1 ]College of Mechanical Engineering, Xi'an University of Science and Technology, Xi'an, China

[2 ]School of Rehabilitation, Kunming Medical University, Kunming, China

Article

DOI: 10.1109/TPWRD.2023.3279891

SO-VID: 005761c8-19f2-4cb3-88fa-fcdeb848510f

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https://doi.org/10.15223/policy-029

https://doi.org/10.15223/policy-037

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Detection of Foreign Objects Intrusion Into Transmission Lines Using Diverse Generation Model

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TKD - Transmission Kikuchi Diffraction

Most cited references 49

Deep Residual Learning for Image Recognition

ImageNet classification with deep convolutional neural networks

Densely Connected Convolutional Networks

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