Transmission Line Fault Insulator Detection Based on GAN- Faster RCNN-Reference-Cited by-同舟云学术

Transmission Line Fault Insulator Detection Based on GAN- Faster RCNN

Published:2023-06-21 Issue: Volume: Page:
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Author:

Zhang Yue¹,Xu Yonghui²,Cui Lizhen³

Affiliation:

1. Qinghai Normal University

2. Nanyang University of Technology International Joint Institute of Artificial Intelligence

3. Shandong University

Abstract

Abstract Insulators are essential and numerous components in power transmission lines, but they are also prone to faults. Therefore, it is crucial to detect faults in insulators. Although existing fault detection methods for insulators in power transmission lines have been improved to some extent by continuously modifying their internal structures, traditional detection methods still suffer from low accuracy and limited applicability in practical engineering applications. To address these issues, this study proposes an improved Faster Region Convolutional Neural Network (Faster RCNN) network as a generator for detecting insulator defects in power transmission lines. In addition, an adversarial loss is introduced by building a discriminator to enhance the overall detection capability of the original Faster RCNN model. Experimental results demonstrate that our proposed model outperforms existing insulator fault detection models in terms of accuracy.

Publisher

Research Square Platform LLC

Reference24 articles.

1. Multi-saliency aggregation-based approach for insulator flashover fault detection using aerial images;Zhai Y;Energies

2. Iruansi U et al (2015) “An active contour approach to insulator segmentation,”Proc. AFRICON, Addis Ababa, Ethiopia, pp. 1–5

3. Insulator detection and defect classification using rotation invariant local directional pattern;Jabid T;Int J Adv Comput Sci Appl,2018

4. Peng X et al (2019) “An automatically locating method for insulator object based on CNN,”Geomatics Inf. Sci. Wuhan Univ., vol. 44, no. 4,pp. 563–569,

5. Tao X, Zhang D, Wang Z, Liu X, Zhang H, Xu D (2020) “Detection of power line insulator defects using aerial images analyzed with convolutional neural networks,” IEEE Trans. Syst., Man, Cybern., Syst.,vol. 50, no. 4, pp. 1486–1498, Apr.