Abstract
The power system of China is still composed of power generation, transmission, substation, power distribution and other links. To ensure the safety and stability of transmission lines is an important part of large-scale transmission process, and the insulators are important in the transmission line. The existing parts, such as surface contamination, cracks, damage and other surface defects seriously threaten the operation safety of the power grid. Faults caused by insulator defects are currently the highest proportion of power system faults, so the surface defects of insulators are detected and timely completion of fault repair becomes more important. In this regard, this paper proposes a target detection algorithm based on YOLO V3 (You Only Look Once: Real-Time Object Detection), which utilizes the powerful learning ability of deep convolutional neural networks and a large number of data annotation samples. The image of the insulator photo-graphed by the machine is detected and classified, finally the intelligent detection of the intact insulator and the defective insulator is realized. The experimental results show that the YOLO V3 based insulator defect detection method can effectively identify the defective insulator strings from the aerial image of the drone. Compared with the previous insulator defect identification method, the accuracy and detection time are significantly improved, and it can realize the intelligent detection of intact insulators and defective insulators.
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References
Wang, L.J.: Research on insulator fault detection algorithm based on deep convolutional neural network. Tianjin University of Technology (2018)
Wang, J., Jiao, X.X.: Common faults and troubleshooting methods of insulator. Electr. Drive Autom. 36(06), 50–53 (2014)
Li, L.X.: The UAV intelligent inspection of transmission lines. In: Proceedings of 2015 International Conference on Advances in Mechanical Engineering and Industrial Informatics (AMEII 2015), p. 4. Computer Science and Electronic Technology International Society (2015)
Li, X., et al.: Application and demand analysis of UAV in power industry. Electr. Appl. 34(S2), 773–775 (2015)
Peng, X.Y., Liu, Z.J., Mai, X.M., Luo, Z.B., Wang, K., Xie, X.W.: A transmission line inspection system based on remote sensing: system and its key technologies. Remote Sens. Inform. 30(01), 51–57 (2015)
Zhai, Y.J., Wang, D., Wu, Y., Cheng, H.Y.: Two-stage recognition method of aerial insulator images based on skeleton extraction. J. North China Electr. Power Univ. 42(03), 105–110 (2015)
Yao, J.G., et al.: Algorithm research of automatically extracting the area of insulator from infrared image and state identification. J. Hunan Univ. (Nat. Sci.) 42(02), 74–80 (2015)
Fu, W.P., Shi, F.X., Wang, W., Liu, Y.P., Ji, X.X., Pei, S.T.: Single insulator fault diagnosis method based on color matrix. Insul. Surge Arresters 285(05), 226–232+240 (2018)
Liu, L.S., Wang, L.M., Mei, H.W., Guo, C.J.: Defects detection method for composite insulator based on shearing speckle interferometry. In: Proceedings of the CSEE, pp. 1–8 (2019)
Chen, J.W., Zhou, X., Zhang, R., Zhang, D.: Aerial insulator detection based on U-net network. J. Shaanxi Univ. Sci. Technol. 36(04), 153–157 (2018)
Silver, D., Huang, A., Maddison, C.J., et al.: Mastering the game of Go with deep neural networks and tree search. Nature 529(7587), 484–489 (2016)
Murthy, V.S., Gupta, S., Mohanta, D.K.: Digital image processing approach using combined wavelet hidden markov model for well-being analysis of insulators. IET Image Process. 5(2), 171–183 (2011)
Wang, M., Du, Y., Zhang, Z.R.: Study on power transmission lines inspection using unmanned aerial vehicle and image recognition of insulator defect. J. Eelectron. Meas. Instrum. 29(12), 1862–1869 (2015)
You Only Look Once: Unified, Real-Time Object Detection. http://pjreddie.com/yolo/. Accessed 09 May 2016
YOLO9000: Better, Faster, Stronger. http://pjreddie.com/yolo/. Accessed 25 Dec 2016
Acknowledgements
This research was supported by Tianjin Enterprise Science and Technology Commissioner Project 18JCTPJC60500, Tianjin Natural Science Fund Project 18JCYBJC85600, Qinghai Key Laboratory of Internet of Things Project (2017-ZJ-Y21), Infrared Radiation Heating Intelligent Control and Basic Ventilation Auxiliary Engineering System Development of No. 2 Section of Changchun Metro Line 2 (hx 2018-37).
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© 2019 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Guo, F., Hao, K., Xia, M., Zhao, L., Wang, L., Liu, Q. (2019). Detection of Insulator Defects Based on YOLO V3. In: Han, S., Ye, L., Meng, W. (eds) Artificial Intelligence for Communications and Networks. AICON 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 287. Springer, Cham. https://doi.org/10.1007/978-3-030-22971-9_25
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DOI: https://doi.org/10.1007/978-3-030-22971-9_25
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