Design of mountain fire prevention monitoring system for transmission lines based on machine vision algorithms
-
Shunhu Dong
,
Lishan Ma
Abstract
With the continuous development of society, power grid has become a major guarantee for social and economic development, and its coverage is also expanding. The power grid not only covers some developed areas, but also extends to remote mountainous areas with complex changes in geological and climatic conditions. However, at the same time, line failures and power outages caused by mountain fires are becoming increasingly frequent, which poses a significant threat to the safety and stable operation of the power system. Therefore, how to solve the problem of mountain fire on transmission lines has become an important topic today. The main purpose of this study is to achieve effective monitoring of transmission line mountain fires and provide more assurance for the stable operation of the power system. This article discussed the transmission line and traditional mountain fire prevention monitoring system, and combined the current hot machine vision algorithm to design the overall mountain fire prevention monitoring system, focusing on analyzing the differences between this system and traditional systems. Through experiments, it was found that the monitoring accuracy of the mountain fire prevention monitoring system designed in this article has been improved by 10.6 % compared to the traditional system at location 4, 7.6 % at location 6, and 7.9 % at location 7. At the same time, the real-time performance and anti-interference performance of this system were also better than traditional systems, and the overall performance of the system was better. Practice has proved that the system can effectively overcome the interference of external environment, and monitor and identify the fire situation in real time and accurately, ensuring the safety of transmission lines. At the same time, the research on machine vision algorithms in this paper can also help promote the more perfect development of power systems. Practice has proved that this system can effectively overcome the interference of the external environment, and continuously and accurately detect and identify the fire situation.
-
Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.
-
Research funding: None declared.
-
Conflict of interest statement: The authors declare no conflicts of interest regarding this article.
References
1. Poletti, C, Dioszegi, G, Nyongesa, KW, Vacik, H, Barbujani, M, Kigomo, JN. Characterization of forest fires to support monitoring and management of Mount Kenya Forest. Mt Res Dev 2019;39:1–2. https://doi.org/10.1659/mrd-journal-d-18-00104.1.Search in Google Scholar
2. Thompson, MP, MacGregor, DG, Dunn, CJ, Calkin, DE, Phipps, J. Rethinking the wildland fire management system. J For 2018;116:382–90. https://doi.org/10.1093/jofore/fvy020.Search in Google Scholar
3. Attri, V, Dhiman, R, Sarvade, S. A review on status, implications and recent trends of forest fire management. Arch Agri Environ Sci 2020;5:592–602. https://doi.org/10.26832/24566632.2020.0504024.Search in Google Scholar
4. Jazebi, S, De Leon, F, Nelson, A. Review of wildfire management techniques–part I: causes, prevention, detection, suppression, and data analytics. IEEE Trans Power Deliv 2019;35:430–9. https://doi.org/10.1109/tpwrd.2019.2930055.Search in Google Scholar
5. Qing, YAO, Feng, J, Tang, J, Xu, W, Zhu, X, Yang, B, et al.. Development of an automatic monitoring system for rice light-trap pests based on machine vision. J Integr Agric 2020;19:2500–13. https://doi.org/10.1016/s2095-3119(20)63168-9.Search in Google Scholar
6. Peng, R, Liu, J, Fu, X, Liu, C, Zhao, L. Application of machine vision method in tool wear monitoring. Int J Adv Manuf Technol 2021;116:1357–72. https://doi.org/10.1007/s00170-021-07522-4.Search in Google Scholar
7. Forcan, M, Banjanin, M, Vukovic, G. Advanced teaching method for balanced operations of overhead transmission lines based on simulations and experiment. Int J Electr Eng Educ 2018;55:14–30. https://doi.org/10.1177/0020720917750955.Search in Google Scholar
8. Lee, YJ, Lin, TC, Liu, CW. Multi-terminal nonhomogeneous transmission line fault location utilizing synchronized data. IEEE Trans Power Deliv 2019;34:1030–8. https://doi.org/10.1109/tpwrd.2018.2890337.Search in Google Scholar
9. Mahin, AU, Islam, SN, Ahmed, F, Hossain, MF. Measurement and monitoring of overhead transmission line sag in smart grid: a review. IET Gener Transm Distrib 2022;16:1–8. https://doi.org/10.1049/gtd2.12271.Search in Google Scholar
10. Boer, MM, de Dios, VR, Bradstock, RA. Unprecedented burn area of Australian mega forest fires. Nat Clim Change 2020;10:171–2. https://doi.org/10.1038/s41558-020-0716-1.Search in Google Scholar
11. Kumar, P. Improvement and implementation of machining and positioning method of intelligent construction machinery components relying on machine vision. Kinet Mech Eng 2021;2:45–53. https://doi.org/10.38007/KME.2021.020206.Search in Google Scholar
12. Khan, IA. Video monitoring system for natural environment protection area supporting image recognition. J Environ Prot 2021;2:31–9. https://doi.org/10.38007/NEP.2021.020304.Search in Google Scholar
13. Parks, SA, Holsinger, LM, Miller, C, Parisien, M. Analog‐based fire regime and vegetation shifts in mountainous regions of the western US. Ecography 2018;41:910–21. https://doi.org/10.1111/ecog.03378.Search in Google Scholar
14. Al-Dhief, FT, Sabri, N, Fouad, S, Latiff, NA, Albader, MAA. A review of forest fire surveillance technologies: mobile ad-hoc network routing protocols perspective. J King Saud Univ Comput Inf Sci 2019;31:135–46. https://doi.org/10.1016/j.jksuci.2017.12.005.Search in Google Scholar
15. Xu, G, Wang, Z, Zhou, J, Li, Z, Zhan, Y, Zhao, H, et al.. Rotor loss and thermal analysis of synchronous condenser under single-phase short-circuit fault in the transmission line. J IEEE Trans Energy Convers 2021;37:274–85. https://doi.org/10.1109/TEC.2021.3109608.Search in Google Scholar
16. Huang, B, Ma, S, Wang, P, Wang, H, Yang, J, Guo, X, et al.. Research and implementation of machine vision technologies for empty bottle inspection systems. Eng Sci Technol Int J 2018;21:159–69. https://doi.org/10.1016/j.jestch.2018.01.004.Search in Google Scholar
17. Coffey, VC. Machine vision: the eyes of industry 4.0. Opt Photon News 2018;29:42–9. https://doi.org/10.1364/opn.29.7.000042.Search in Google Scholar
18. Ren, Z, Fang, F, Yan, N, Wu, Y. State of the art in defect detection based on machine vision. Int J Precis Eng Manuf Green Technol 2022;9:661–91. https://doi.org/10.1007/s40684-021-00343-6.Search in Google Scholar
© 2023 Walter de Gruyter GmbH, Berlin/Boston
Articles in the same Issue
- Frontmatter
- Research Articles
- Intelligent identification algorithm and key point detection of abnormal vibration of transmission tower based on machine learning
- Design and development of power data service platform based on multi dimension
- Evaluation on power marketing decision evaluation based on Bayesian network
- Power monitoring data access control system based on BP neural network
- Investigation and application of key technologies of aggregated flash payment based on marketing blockchain in the context of massive distributed generation grid connection
- Research on RBF neural network adaptive control of three-point contactless measuring device for CNC roller grinder
- Measurement of surface vibration signal of 500 kV transformer and analysis of its frequency characteristics
- Evaluation on key technologies for the construction of low-carbon index of electric power based on “double carbon”
- Application scenario evaluation of modified converter for quadratic Boost high gain DC-DC: taking the constant off time control mode as an example
- Efficiency of artificial intelligence automatic control system and data processing unit based on edge computing technology
- Design of mountain fire prevention monitoring system for transmission lines based on machine vision algorithms
Articles in the same Issue
- Frontmatter
- Research Articles
- Intelligent identification algorithm and key point detection of abnormal vibration of transmission tower based on machine learning
- Design and development of power data service platform based on multi dimension
- Evaluation on power marketing decision evaluation based on Bayesian network
- Power monitoring data access control system based on BP neural network
- Investigation and application of key technologies of aggregated flash payment based on marketing blockchain in the context of massive distributed generation grid connection
- Research on RBF neural network adaptive control of three-point contactless measuring device for CNC roller grinder
- Measurement of surface vibration signal of 500 kV transformer and analysis of its frequency characteristics
- Evaluation on key technologies for the construction of low-carbon index of electric power based on “double carbon”
- Application scenario evaluation of modified converter for quadratic Boost high gain DC-DC: taking the constant off time control mode as an example
- Efficiency of artificial intelligence automatic control system and data processing unit based on edge computing technology
- Design of mountain fire prevention monitoring system for transmission lines based on machine vision algorithms
