Electric substation inspection: YOLOv5 in hotspot detection through thermal imaging

This is an outdated version published on 2024-01-16. Read the most recent version.

PDF (Spanish) HTML (Spanish) PDF HTML

Published: Jan 16, 2024

Updated: 2024-01-16

Versions:

2024-05-17 (4)

2024-01-23 (3)

2024-01-16 (2)

2024-01-01 (1)

DOI: https://doi.org/10.17163/ings.n31.2024.04

Keywords:

Electrical substations, Hot spots, Object detection, Thermal images, Transfer learning, YOLOv5

Daniel A. Pérez-Aguilar

https://orcid.org/0000-0003-4514-2873

Jair. M Pérez-Aguilar

https://orcid.org/0009-0009-0875-488X

Andy P. Pérez-Aguilar

https://orcid.org/0009-0008-0390-1038

Redy H. Risco-Ramos

https://orcid.org/0000-0002-3491-6020

Manuel E. Malpica-Rodriguez

https://orcid.org/0000-0002-5534-5116

Abstract

Substations are key facilities within an electrical system, untimely failures tend to cause low quality and negative effects on the electrical supply. An early indicator of potential electrical equipment failure is the appearance of hot spots; therefore, its detection and subsequent programmed correction avoids incurring in major failures and unnecessary operation stops. In this research, 64 experiments of the YOLOv5 algorithm were carried out, with the purpose of proposing an automated computer vision mechanism for the detection of hot spots in thermal images of electrical substations. The best results show a mAP value of 81.99%, which were obtained with the YOLOv5m algorithm and the transfer learning application. These results leave a basis to deepen and improve the performance of the algorithm by varying other hyperparameters to those considered in this study.

Issue

No. 31 (2024): january-june

Section

Electrical Engineering

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

The Universidad Politécnica Salesiana of Ecuador preserves the copyrights of the published works and will favor the reuse of the works. The works are published in the electronic edition of the journal under a Creative Commons Attribution/Noncommercial-No Derivative Works 4.0 Ecuador license: they can be copied, used, disseminated, transmitted and publicly displayed.

The undersigned author partially transfers the copyrights of this work to the Universidad Politécnica Salesiana of Ecuador for printed editions.

It is also stated that they have respected the ethical principles of research and are free from any conflict of interest. The author(s) certify that this work has not been published, nor is it under consideration for publication in any other journal or editorial work.

The author (s) are responsible for their content and have contributed to the conception, design and completion of the work, analysis and interpretation of data, and to have participated in the writing of the text and its revisions, as well as in the approval of the version which is finally referred to as an attachment.

References

S. Hussain, J. Hernández Fernández, A. K. Al-Ali, and A. Shikfa, “Vulnerabilities and countermeasures in electrical substations,” International Journal of Critical Infrastructure Protection, vol. 33, p. 100406, 2021. [Online]. Available: https://doi.org/10.1016/J.IJCIP.2020.100406

Y.-E. Bouffard-Vercelli and B. André, “Future architectures of electrical substations,” in 2021 Petroleum and Chemical Industry Conference Europe (PCIC Europe), 2021. [Online]. Available: https://doi.org/10.23919/PCICEurope50407.2021.9805424

W. Pavon, E. Inga, S. Simani, and M. Nonato, “A review on optimal control for the smart grid electrical substation enhancing transition stability,” Energies, vol. 14, no. 24, 2021. [Online]. Available: https://doi.org/10.3390/en14248451

M. Lin, L. Fu, F. Zeng, G. Yang, and M. Sun, “Design of distributed substation high voltage electrical equipment online monitoring system based on image segmentation technology,” Journal of Physics: Conference Series, vol. 2143, no. 1, p. 012001, dec 2021. [Online]. Available: https://dx.doi.org/10.1088/1742-6596/2143/1/012001

M. A. Haq, D. Kurniawan Danu, Syafii, and Muhardika, “Mitigation of the potential for sudden high-temperature hotspots on substation equipment,” in 2023 4th International Conference on High Voltage Engineering and Power Systems (ICHVEPS), 2023, pp. 194–198. [Online]. Available: https://doi.org/10.1109/ICHVEPS58902.2023.10257349

S. Y. Lee and S. S. Teoh, “A survey on infrared thermography based automatic electrical fault diagnosis techniques,” in 10th International Conference on Robotics, Vision, Signal Processing and Power Applications, M. A. M. Zawawi, S. S. Teoh, N. B. Abdullah, and M. I. S. Mohd Sazali, Eds. Singapore: Springer Singapore, 2019, pp. 537–542. [Online]. Available: https://doi.org/10.1007/978-981-13-6447-1_68

F. Ciampa, P. Mahmoodi, F. Pinto, and M. Meo, “Recent advances in active infrared thermography for non-destructive testing of aerospace components,” Sensors, vol. 18, no. 2, 2018. [Online]. Available: https://doi.org/10.3390/s18020609

M. Haenlein and A. Kaplan, “A brief history of artificial intelligence: On the past, present, and future of artificial intelligence,” California Management Review, vol. 61, no. 4, pp. 5–14, 2019. [Online]. Available: https://doi.org/10.1177/0008125619864925

A. Ghahramani, G. Castro, S. A. Karvigh, and B. Becerik-Gerber, “Towards unsupervised learning of thermal comfort using infrared thermography,” Applied Energy, vol. 211, pp. 41–49, 2018. [Online]. Available: https://doi.org/10.1016/j.apenergy.2017.11.021

Y. J. Wai, Z. bin Mohd Yussof, S. I. bin Salim, and L. K. Chuan, “Fixed point implementation of Tiny-YOLO-v2 using OpenCL on FPGA,” International Journal of Advanced Computer Science and Applications, vol. 9, no. 10, 2018. [Online]. Available: http://dx.doi.org/10.14569/IJACSA.2018.091062

Y. Xiao, Z. Tian, J. Yu, Y. Zhang, S. Liu, S. Du, and X. Lan, “A review of object detection based on deep learning,” Multimedia Tools and Applications, vol. 79, no. 33, pp. 23 729–23 791, Sep 2020. [Online]. Available: https://doi.org/10.1007/s11042-020-08976-6

S. Srivastava, A. V. Divekar, C. Anilkumar, I. Naik, V. Kulkarni, and V. Pattabiraman, “Comparative analysis of deep learning image detection algorithms,” Journal of Big Data, vol. 8, no. 1, p. 66, May 2021. [Online]. Available: https://doi.org/10.1186/s40537-021-00434-w

D. Dlužnevskij, P. Stefanovic, and S. Ramanauskaite, “Investigation of YOLOv5 efficiency in iphone supported systems,” Baltic Journal of Modern Computing, vol. 9, no. 3, pp. 333–344, 2021. [Online]. Available: https://doi.org/10.22364/bjmc.2021.9.3.07

Z. Ma, Y. Wan, J. Liu, R. An, and L. Wu, “A kind of water surface multi-scale object detection method based on improved YOLOv5 network,” Mathematics, vol. 11, no. 13, 2023. [Online]. Available: https://doi.org/10.3390/math11132936

G. Liu, J. C. Nouaze, P. L. Touko Mbouembe, and J. H. Kim, “YOLO-Tomato: A robust algorithm for tomato detection based on YOLOv3,” Sensors, vol. 20, no. 7, 2020. [Online]. Available: https://doi.org/10.3390/s20072145

X. Gong, Q. Yao, M. Wang, and Y. Lin, “A deep learning approach for oriented electrical equipment detection in thermal images,” IEEE Access, vol. 6, pp. 41 590–41 597, 2018. [Online]. Available: https://doi.org/10.1109/ACCESS.2018.2859048

X. Li, “Design of infrared anomaly detection for power equipment based on YOLOv3,” in 2019 IEEE 3rd Conference on Energy Internet and Energy System Integration (EI2), 2019, pp. 2291–2294. [Online]. Available: https://doi.org/10.1109/EI247390.2019.9061852

A. Greco, C. Pironti, A. Saggese, M. Vento, and V. Vigilante, “A deep learning based approach for detecting panels in photovoltaic plants,” in Proceedings of the 3rd International Conference on Applications of Intelligent Systems, ser. APPIS

New York, NY, USA: Association for Computing Machinery, 2020. [Online]. Available: https://doi.org/10.1145/3378184.3378185

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 Transactions on Very Large Scale Integration (VLSI) Systems, vol. 27, no. 8, pp. 1861–1873, 2019. [Online]. Available: https://doi.org/10.1109/TVLSI.2019.2905242

D. Fan, D. Liu, W. Chi, X. Liu, and Y. Li, “Improved ssd-based multi-scale pedestrian detection algorithm,” in Advances in

D Image and Graphics Representation, Analysis, Computing and Information Technology, R. Kountchev, S. Patnaik, J. Shi, and M. N. Favorskaya, Eds. Singapore: Springer Singapore, 2020, pp. 109–118. [Online]. Available: https://doi.org/10.1007/978-981-15-3867-4_14

S. Ren, K. He, R. Girshick, and J. Sun, “Faster R-CNN: Towards real-time object detection with region proposal networks,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 39, no. 6, pp. 1137–1149, 2017. [Online]. Available: https://doi.org/10.1109/TPAMI.2016.2577031

W. Chen, H. Huang, S. Peng, C. Zhou, and C. Zhang, “YOLO-face: a real-time face detector,” The Visual Computer, vol. 37, no. 4, pp. 805–813, Apr 2021. [Online]. Available: https://doi.org/10.1007/s00371-020-01831-7

S. A. Sánchez, H. J. Romero, and A. D. Morales, “A review: Comparison of performance metrics of pretrained models for object detection using the tensorflow framework,” IOP Conference Series: Materials Science and Engineering, vol. 844, no. 1, p. 012024, may 2020. [Online]. Available: https://dx.doi.org/10.1088/1757-899X/844/1/012024

J. Xue, F. Cheng, Y. Li, Y. Song, and T. Mao, “Detection of farmland obstacles based on an improved YOLOv5s algorithm by using CIoU and anchor box scale clustering,” Sensors, vol. 22, no. 5, 2022. [Online]. Available: https://doi.org/10.3390/s22051790

A. Li, S. Sun, Z. Zhang, M. Feng, C. Wu, and W. Li, “A multi-scale traffic object detection algorithm for road scenes based on improved YOLOv5,” Electronics, vol. 12, no. 4, 2023. [Online]. Available: https://doi.org/10.3390/electronics12040878

J. Shi, J. Yang, and Y. Zhang, “Research on steel surface defect detection based on YOLOv5 with attention mechanism,” Electronics, vol. 11, no. 22, 2022. [Online]. Available: https://doi.org/10.3390/electronics11223735

D. A. Pérez-Aguilar, R. H. Risco-Ramos, and L. Casaverde-Pacherrez, “Transfer learning en la clasificación binaria de imágenes térmicas,” INGENIUS, no. 26, pp. 71–86, 2021. [Online]. Available: https://doi.org/10.17163/ings.n26.2021.07

A. Yan-Tak Ng. (2022) Unbiggen AI. IEEE Spectrum. IEEE Spectrum. [Online]. Available: https://bit.ly/3RNNvsr

R. Padilla, W. L. Passos, T. L. B. Días, S. L. Netto, and E. A. B. da Silva, “A comparative analysis of object detection metrics with a companion open-source toolkit,” Electronics, vol. 10, no. 3, 2021. [Online]. Available: https://doi.org/10.3390/electronics10030279

Article Sidebar

Main Article Content

Abstract

Article Details

References