AI Chat Paper
Note: Please note that the following content is generated by AMiner AI. SciOpen does not take any responsibility related to this content.
{{lang === 'zh_CN' ? '文章概述' : 'Summary'}}
{{lang === 'en_US' ? '中' : 'Eng'}}
Chat more with AI
PDF (11.1 MB)
Collect
Submit Manuscript AI Chat Paper
Show Outline
Outline
Show full outline
Hide outline
Outline
Show full outline
Hide outline
Open Access

Enhancing Power Line Insulator Health Monitoring with a Hybrid Generative Adversarial Network and YOLO3 Solution

Department of Electronics and Communication Engineering, Koneru Lakshmaiah Education Foundation, Hyderabad 500075, India
Department of Electrical and Electronics Engineering, National Institute of Technology Nagaland, Dimapur 797103, India
Show Author Information

Abstract

In the critical field of electrical grid maintenance, ensuring the integrity of power line insulators is a primary concern. This study introduces an innovative approach for monitoring the condition of insulators using aerial surveillance via drone-mounted cameras. The proposed method is a composite deep learning framework that integrates the “You Only Look Once” version 3 (YOLO3) model with deep convolutional generative adversarial networks (DCGAN) and super-resolution generative adversarial networks (SRGAN). The YOLO3 model excels in rapidly and accurately detecting insulators, a vital step in assessing their health. Its effectiveness in distinguishing insulators against complex backgrounds enables prompt detection of defects, essential for proactive maintenance. This rapid detection is enhanced by DCGAN’s precise classification and SRGAN’s image quality improvement, addressing challenges posed by low-resolution drone imagery. The framework’s performance was evaluated using metrics such as sensitivity, specificity, accuracy, localization accuracy, damage sensitivity, and false alarm rate. Results show that the SRGAN+DCGAN+YOLO3 model significantly outperforms existing methods, with a sensitivity of 98%, specificity of 94%, an overall accuracy of 95.6%, localization accuracy of 90%, damage sensitivity of 92%, and a reduced false alarm rate of 8%. This advanced hybrid approach not only improves the detection and classification of insulator conditions but also contributes substantially to the maintenance and health of power line insulators, thus ensuring the reliability of the electrical power grid.

References

[1]

S. Anjum, S. Jayaram, A. El-Hag, and A. N. Jahromi, Detection and classification of defects in ceramic insulators using RF antenna, IEEE Trans. Dielect. Electr. Insul., vol. 24, no. 1, pp. 183–190, 2017.

[2]
K. Marimuthu, S. Vynatheya, N. Vasudev, and P. Raja, Quality Analysis of Ceramic Insulators Under Electro Thermal Stresses, in Proc. 2019 Int. Conf. on High Voltage Engineering and Technology (ICHVET), Hyderabad, India, 2019, pp. 1–6.
[3]

M. T. Gencoglu and M. Uyar, Prediction of flashover voltage of insulators using least squares support vector machines, Expert Syst. Appl., vol. 36, no. 7, pp. 10789–10798, 2009.

[4]
L. Yang, F. Zhang, and Y. Hao, Effects of structure and material of polluted insulators on the wetting characteristics, IET Sci. Meas. Technol., vol. 13, no. 2, pp. 131–138, 2018.
[5]

G. Montoya, I. Ramirez, and J. I. Montoya, Correlation among ESDD, NSDD and leakage current in distribution insulators, IEE Proc., Gener. Transm. Distrib., vol. 151, no. 3, p. 334, 2004.

[6]

G. H. Vaillancourt, J. P. Bellerive, M. St-Jean, and C. Jean, New live line tester for porcelain suspension insulators on high-voltage power lines, IEEE Trans. Power Deliv., vol. 9, no. 1, pp. 208–219, 1994.

[7]
K. L. Wong, Application of very-high-frequency (VHP) method to ceramic insulators, IEEE Trans. Dielect. Electr. Insul., vol. 11, no. 6, pp. 1057–1064, 2004.
[8]

X. Ouyang, Z. Jia, S. Yang, X. Shang, X. Wang, H. Chen, D. Zhou, and R. Liu, Influence of algae growth on the external insulation performance of HVDC insulators, IEEE Trans. Dielect. Electr. Insul., vol. 25, no. 1, pp. 263–271, 2018.

[9]

V. Murthy, K. Tarakanath, D. Mohanta, and S. Gupta, Insulator condition analysis for overhead distribution lines using combined wavelet support vector machine (SVM), IEEE Trans. Dielect. Electr. Insul., vol. 17, no. 1, pp. 89–99, 2010.

[10]

R. Li, H. Gu, B. Hu, and Z. She, Multi-feature fusion and damage identification of large generator stator insulation based on lamb wave detection and SVM method, Sensors, vol. 19, no. 17, pp. 3733, 2019.

[11]

D. Mussina, A. Irmanova, P. K. Jamwal, and M. Bagheri, Multi-modal data fusion using deep neural network for condition monitoring of high voltage insulator, IEEE Access, vol. 8, pp. 184486–184496, 2020.

[12]

D. T. Nguyen, T. N. Nguyen, H. Kim, and H. J. Lee, A high-throughput and power-efficient FPGA implementation of YOLO CNN for object detection, IEEE Trans. VLSI Syst., vol. 27, no. 8, pp. 1861–1873, 2019.

[13]

S. Khan, N. Islam, Z. Jan, I. Ud Din, and J. J. P. C. Rodrigues, A novel deep learning based framework for the detection and classification of breast cancer using transfer learning, Pattern Recognit. Lett., vol. 125, pp. 1–6, 2019.

[14]

X. Tao, D. Zhang, Z. Wang, X. Liu, H. Zhang, and D. Xu, 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, 2020.

[15]

M. T. Gençoğlu and M. Cebeci, Investigation of pollution flashover on high voltage insulators using artificial neural network, Expert Syst. Appl., vol. 36, no. 4, pp. 7338–7345, 2009.

[16]

A. Krizhevsky, I. Sutskever, and G. E. Hinton, ImageNet classification with deep convolutional neural networks, Commun. ACM, vol. 60, no. 6, pp. 84–90, 2017.

[17]

L. Singh, A. Alam, K. V. Kumar, D. Kumar, P. Kumar, and Z. A. Jaffery, Design of thermal imaging-based health condition monitoring and early fault detection technique for porcelain insulators using machine learning, Environ. Technol. Innov., vol. 24, p. 102000, 2021.

[18]

S. F. Stefenon, L. O. Seman, N. F. Sopelsa Neto, L. H. Meyer, V. C. Mariani, and L. S. Coelho, Group method of data handling using Christiano–Fitzgerald random walk filter for insulator fault prediction, Sensors, vol. 23, no. 13, p. 6118, 2023.

[19]
I. J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, Generative adversarial nets, in Proc. 27th Int. Conf. Neural Information Processing Systems - Volume 2, Montreal, Canada, 2014, pp. 2672–2680.
[20]

Y. LeCun, Y. Bengio, and G. Hinton, Deep learning, Nature, vol. 521, no. 7553, pp. 436–444, 2015.

[21]
I. J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, Generative adversarial nets, in Proc. 27th Int. Conf. Neural Information Processing Systems - Volume 2, Montreal, Canada, 2014, pp. 2672–2680.
[22]
H. Emami, M. M. Aliabadi, M. Dong, and R. B. Chinnam, SPA-GAN: Spatial attention GAN for image-to-image translation, IEEE Trans. Multimedia, vol. 23, pp. 391–401, 2021.
[23]

L. Gao, D. Chen, Z. Zhao, J. Shao, and H. T. Shen, Lightweight dynamic conditional GAN with pyramid attention for text-to-image synthesis, Pattern Recognit., vol. 110, p. 107384, 2021.

[24]

Y. Yu, Z. Huang, F. Li, H. Zhang, and X. Le, Point Encoder GAN: A deep learning model for 3D point cloud inpainting, Neurocomputing, vol. 384, pp. 192–199, 2020.

[25]
U. Babawuro, B. Zou, and B. Xu, High resolution satellite imagery rectification using Bi-linear interpolation method for geometric data extraction, in Proc. Second Int. Conf. Intelligent System Design and Engineering Application, Sanya, China, 2012.
[26]
J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, You only look once: Unified, real-time object detection, in Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, 2016.
[27]
J. Redmon and A. Farhadi, YOLO9000: Better, faster, stronger, in Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR). Honolulu, HI, USA, 2017.
[28]
J. Redmon, and A. Farhadi, YOLO3: An incremental improvement, arXiv preprint arXiv:1804.02767, 2018.
[29]
T. Y. Lin, P. Dollar, R. Girshick, K. He, B. Hariharan, and S. Belongie, Feature pyramid networks for object detection, in Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA, 2017.
[30]
C. Dong, C. C. Loy, K. He, and X. Tang, Learning a deep convolutional network for image super-resolution, in European Conf. Computer Vision, Zurich, Switzerland, 2014.
[31]
C. Ledig, L. Theis, F. Husz´ar, J. Caballero, A. Cunningham, A. Acosta, A. Aitken, A. Tejani, J. Totz, Z. Wang, W. Shi, Photo-realistic single image super-resolution using a generative adversarial network, in Proc. IEEE Conference on Computer Vision and Pattern Recognition 2017, Honolulu, HI, USA, pp. 4681–4690, 2017.
[32]
D. Sarkar and S. K. Gunturi, Wind turbine blade structural state evaluation by hybrid object detector relying on deep learning models, Journal of Ambient Intelligence and Humanized Computing, pp. 1–4, 2020.
[33]

Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, Image quality assessment: From error visibility to structural similarity, IEEE Trans. Image Process., vol. 13, no. 4, pp. 600–612, 2004.

Tsinghua Science and Technology
Pages 1796-1809
Cite this article:
Akella R, Gunturi SK, Sarkar D. Enhancing Power Line Insulator Health Monitoring with a Hybrid Generative Adversarial Network and YOLO3 Solution. Tsinghua Science and Technology, 2024, 29(6): 1796-1809. https://doi.org/10.26599/TST.2023.9010137

126

Views

12

Downloads

0

Crossref

0

Web of Science

0

Scopus

0

CSCD

Altmetrics

Received: 20 August 2023
Revised: 14 December 2023
Accepted: 26 December 2023
Published: 20 June 2024
© The Author(s) 2024.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).

Return