Electric substation inspection: YOLOv5 in hotspot detection through thermal imaging

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Published: Jan 1, 2024
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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

Main Article Content

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.

Article Details

Issue
No. 31 (2024): january-june
Section
Electrical Engineering
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References

V. B. Núñez, R. Velandia, F. Hernández, J. Meléndez, and H. Vargas, “Atributos Relevantes para el Diagnóstico Automático de Eventos de Tensión en Redes de Distribución de Energía Eléctrica,” Revista Iberoamericana de Automática e Informática Industrial RIAI, vol. 10, no. 1, pp. 73–84, Jan. 2013, doi: 10.1016/J.RIAI.2012.11.007.

M. Iglesias-Urkia, D. Casado-Mansilla, S. Mayer, J. Bilbao, and A. Urbieta, “Integrating Electrical Substations Within the IoT Using IEC 61850, CoAP, and CBOR,” IEEE Internet Things J, vol. 6, no. 5, pp. 7437–7449, Oct. 2019, doi: 10.1109/JIOT.2019.2903344.

Q. Song et al., “Smart substation integration technology and its application in distribution power grid,” CSEE Journal of Power and Energy Systems, vol. 2, no. 4, pp. 31–36, Dec. 2016, doi: 10.17775/CSEEJPES.2016.00046.

T. Ribeiro, M. Araujo, A. Pereira, and P. R. D. Monteiro, “Comparison of Industrial Substation Arrangements,” IEEE Latin America Transactions, vol. 18, no. 10, pp. 1834–1841, Oct. 2020, doi: 10.1109/TLA.2020.9387675.

I. Ullah et al., “Predictive Maintenance of Power Substation Equipment by Infrared Thermography Using a Machine-Learning Approach,” Energies 2017, Vol. 10, Page 1987, vol. 10, no. 12, p. 1987, Dec. 2017, doi: 10.3390/EN10121987.

S. Y. Lee and S. S. Teoh, “A Survey on Infrared Thermography Based Automatic Electrical Fault Diagnosis Techniques,” Lecture Notes in Electrical Engineering, vol. 547, pp. 537–542, 2019, doi: 10.1007/978-981-13-6447-1_68.

Z. Wang, G. Y. Tian, M. Meo, and F. Ciampa, “Image processing based quantitative damage evaluation in composites with long pulse thermography,” NDT and E International, vol. 99, pp. 93–104, Oct. 2018, doi: 10.1016/j.ndteint.2018.07.004.

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, p. 609, Feb. 2018, doi: 10.3390/s18020609.

E. Lucchi, “Applications of the infrared thermography in the energy audit of buildings: A review,” Renewable and Sustainable Energy Reviews, vol. 82. Elsevier Ltd, pp. 3077–3090, Feb. 01, 2018. doi: 10.1016/j.rser.2017.10.031.

R. Yang and Y. He, “Optically and non-optically excited thermography for composites: A review,” Infrared Phys Technol, vol. 75, pp. 26–50, Mar. 2016, doi: 10.1016/j.infrared.2015.12.026.

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

C. Bravo, J. Aguilar-Castro, A. Ríos, J. Aguilar-Martin, and F. Rivas, “Arquitectura Basada en Inteligencia Artificial Distribuida para la Gerencia Integrada de Producción Industrial,” Revista Iberoamericana de Automática e Informática Industrial RIAI, vol. 8, no. 4, pp. 405–417, Oct. 2011, doi: 10.1016/J.RIAI.2011.09.013.

M. Haenlein and A. Kaplan, “A Brief History of Artificial Intelligence: On the Past, Present, and Future of Artificial Intelligence,” Calif Manage Rev, vol. 61, no. 4, pp. 5–14, Aug. 2019, doi: 10.1177/0008125619864925.

M. J. Flores, J. M. Armingol M, and A. de la Escalera, “Sistema avanzado de asistencia a la conducción para la detección de la somnolencia,” RIAI - Revista Iberoamericana de Automatica e Informatica Industrial, vol. 8, no. 3, pp. 216–228, 2011, doi: 10.1016/J.RIAI.2011.06.009.

M. Castrillón-Santana, J. Lorenzo-Navarro, and D. Hernández-Sosa, “Conteo de personas con un sensor RGBD comercial,” Revista Iberoamericana de Automática e Informática Industrial RIAI, vol. 11, no. 3, pp. 348–357, Jul. 2014, doi: 10.1016/J.RIAI.2014.05.006.

A. C. Bernal, D. L. A. Ojeda, and M. A. I. Manzano, “Object detection from a range image using sparse keypoint detector technique,” IEEE Latin America Transactions, vol. 16, no. 5, pp. 1532–1538, May 2018, doi: 10.1109/TLA.2018.8408451.

Y. Xiao et al., “A review of object detection based on deep learning,” Multimedia Tools and Applications 2020 79:33, vol. 79, no. 33, pp. 23729–23791, Jun. 2020, doi: 10.1007/S11042-020-08976-6.

J. Kim and J. Cho, “Exploring a Multimodal Mixture-Of-YOLOs Framework for Advanced Real-Time Object Detection,” Applied Sciences 2020, Vol. 10, Page 612, vol. 10, no. 2, p. 612, Jan. 2020, doi: 10.3390/APP10020612.

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. 41590–41597, Jul. 2018, doi: 10.1109/ACCESS.2018.2859048.

X. Li, “Design of Infrared Anomaly Detection for Power Equipment Based on YOLOv3,” 2019 3rd IEEE Conference on Energy Internet and Energy System Integration: Ubiquitous Energy Network Connecting Everything, EI2 2019, pp. 2291–2294, Nov. 2019, doi: 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,” ACM International Conference Proceeding Series, Jan. 2020, doi: 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 Trans Very Large Scale Integr VLSI Syst, vol. 27, no. 8, pp. 1861–1873, Aug. 2019, doi: 10.1109/TVLSI.2019.2905242.

S. Srivastava, A. V. Divekar, C. Anilkumar, I. Naik, V. Kulkarni, and V. Pattabiraman, “Comparative analysis of deep learning image detection algorithms,” J Big Data, vol. 8, no. 1, pp. 1–27, Dec. 2021, doi: 10.1186/S40537-021-00434-W/TABLES/2.

D. Fan, D. Liu, W. Chi, X. Liu, and Y. Li, “Improved SSD-Based Multi-scale Pedestrian Detection Algorithm,” Smart Innovation, Systems and Technologies, vol. 180, pp. 109–118, 2020, doi: 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 Trans Pattern Anal Mach Intell, vol. 39, no. 6, pp. 1137–1149, Jun. 2017, doi: 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 2020 37:4, vol. 37, no. 4, pp. 805–813, Mar. 2020, doi: 10.1007/S00371-020-01831-7.

M. Andreev et al., “A review: Comparison of performance metrics of pretrained models for object detection using the TensorFlow framework,” IOP Conf Ser Mater Sci Eng, vol. 844, no. 1, p. 012024, May 2020, doi: 10.1088/1757-899X/844/1/012024.

T. Yulin et al., “Improved YOLOv5 Method for Detecting Shipwreck Target with Side-scan Sonar,” Geomatics and Information Science of Wuhan University, vol. 0, no. 0, pp. 0–0, doi: 10.13203/J.WHUGIS20210353.

A. Mohiyuddin et al., “Breast Tumor Detection and Classification in Mammogram Images Using Modified YOLOv5 Network,” Comput Math Methods Med, vol. 2022, 2022, doi: 10.1155/2022/1359019.

Y. Fan, X. Ma, S. Ma, K. Qian, and H. Hao, “Evaluation method of laser jamming effect based on deep learning,” Infrared and Laser Engineering, vol. 50, no. S2, p. 20210323, Oct. 2021, doi: 10.3788/IRLA20210323.

Z. Charouh, A. Ezzouhri, M. Ghogho, and Z. Guennoun, “A Resource-Efficient CNN-Based Method for Moving Vehicle Detection,” Sensors 2022, Vol. 22, Page 1193, vol. 22, no. 3, p. 1193, Feb. 2022, doi: 10.3390/S22031193.

Y. J. Wai, Z. bin M. 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, pp. 506–512, 2018, doi: 10.14569/IJACSA.2018.091062.

I. Martinez-Alpiste, G. Golcarenarenji, Q. Wang, and J. M. Alcaraz-Calero, “A dynamic discarding technique to increase speed and preserve accuracy for YOLOv3,” Neural Comput Appl, vol. 33, no. 16, pp. 9961–9973, Aug. 2021, doi: 10.1007/S00521-021-05764-7/FIGURES/9.

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, doi: 10.22364/BJMC.2021.9.3.07.

G. Liu, J. C. Nouaze, P. L. T. Mbouembe, and J. H. Kim, “YOLO-Tomato: A Robust Algorithm for Tomato Detection Based on YOLOv3,” Sensors 2020, Vol. 20, Page 2145, vol. 20, no. 7, p. 2145, Apr. 2020, doi: 10.3390/S20072145.

D. Pérez-Aguilar, R. Risco-Ramos, and L. Casaverde-Pacherrez, “Transfer learning en la clasificación binaria de imágenes térmicas,” Ingenius. Revista de Ciencia y Tecnología, no. 26, pp. 71–86, Jun. 2021, doi: 10.17163/INGS.N26.2021.07.

A. Yan-Tak Ng, “Unbiggen AI,” Feb. 09, 2022. https://spectrum.ieee.org/andrew-ng-data-centric-ai (accessed Apr. 27, 2022).

R. Padilla, W. L. Passos, T. L. B. Dias, S. L. Netto, and E. A. B. Da Silva, “A Comparative Analysis of Object Detection Metrics with a Companion Open-Source Toolkit,” Electronics 2021, Vol. 10, Page 279, vol. 10, no. 3, p. 279, Jan. 2021, doi: 10.3390/ELECTRONICS10030279.