Title: Multi-scale transmission line defect identification for new power system based on convolutional neural network

Authors: Jinbin Luo; Jian Zhang; Ruchao Liao; Duanjiao Li; Jinchao Guo

Addresses: Machine Patrol Management Center of Guangdong Power Grid Co., Ltd, Guangzhou, 510180, China ' Machine Patrol Management Center of Guangdong Power Grid Co., Ltd, Guangzhou, 510180, China ' Machine Patrol Management Center of Guangdong Power Grid Co., Ltd, Guangzhou, 510180, China ' Machine Patrol Management Center of Guangdong Power Grid Co., Ltd, Guangzhou, 510180, China ' Machine Patrol Management Center of Guangdong Power Grid Co., Ltd, Guangzhou, 510180, China

Abstract: In the face of multiple multi-scale transmission line defects, existing methods have problems with low identification accuracy and long identification time. Therefore, this paper proposes a multi-scale transmission line defect identification for new power system based on convolutional neural network. Firstly, use unmanned aerial vehicles to collect multi-scale images of transmission line defects and correct the collected images. Secondly, this article establishes a unified data centre for storing, managing, and analysing this data. Then, convolutional neural networks are used to extract defect features from multi-scale transmission line images. Finally, the extracted defect features are input into the support vector to complete the identification of multi-scale transmission line defects. The experimental results show that the proposed method can accurately identify various types of transmission line defects and shorten the identification time.

Keywords: data centre; new power system; multi scale; transmission lines; defect identification.

DOI: 10.1504/IJMIC.2025.147943

International Journal of Modelling, Identification and Control, 2025 Vol.46 No.1, pp.21 - 29

Received: 06 Sep 2023
Accepted: 29 Feb 2024
Published online: 11 Aug 2025 *

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