Abstract: The development and progress of modern society cannot be achieved without the support of electric power resources, and at present, electric power is the most important energy source to promote social development and maintain human life. As a key unit under the power distribution and transmission system, the high-voltage cable of the power grid will undertake the task of supplying power resources to the whole power grid. Therefore, based on the transmission line fault diagnosis framework of Wireless Sensor Networks (WSN), a high-voltage cable path condition monitoring scheme using LoRa technology is proposed. Three high-voltage cable condition monitoring periods are proposed according to the difference of high-voltage cable fault rate, and the delay and energy consumption of the high-voltage cable monitoring system are optimized by multi-objective particle swarm algorithm reality. The experimental results show that the proposed high-voltage cable detection technology can switch the working mode according to different environments, and the data communication packet loss rate is less than 5%, while the detection platform has excellent delay performance and energy saving effect. The high-voltage cable status detection solution can effectively solve the problem of blind high-voltage cable channels in high mountain areas of China. The research content has important reference value for the detection of China’s power grid circuit system.

Yang Zhao, Qing Liu, Tong Shang, Yingqiang Shang, Rong Xia and Shuai Shao, “Research on High Voltage Cable Condition Detection Technology based on Wireless Sensor Network” International Journal of Advanced Computer Science and Applications(IJACSA), 13(12), 2022. http://dx.doi.org/10.14569/IJACSA.2022.0131283

@article{Zhao2022,
title = {Research on High Voltage Cable Condition Detection Technology based on Wireless Sensor Network},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2022.0131283},
url = {http://dx.doi.org/10.14569/IJACSA.2022.0131283},
year = {2022},
publisher = {The Science and Information Organization},
volume = {13},
number = {12},
author = {Yang Zhao and Qing Liu and Tong Shang and Yingqiang Shang and Rong Xia and Shuai Shao}
}