Affiliations: [a] State Grid Xinjiang Electric Power Research Institute, Urumqi, Xinjiang, China | [b] Xinjiang Key Laboratory of Extreme Environment Operation and Detection Technology for Power Transmission and Transformation Equipment, Urumqi, Xinjiang, China | [c] State Grid Xinjiang Electric Power Co., Ltd Kashgar Power Supply Company, Kashgar, Xinjiang, China
Correspondence:
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Corresponding author: Mingguan Zhao, State Grid Xinjiang Electric Power Research Institute, Urumqi, Xinjiang 830011, China. E-mail: 15199110705@163.com.
Abstract: With the explosive growth of electricity consumption, the demand for electricity by electricity users is increasing. As a core component of power supply, the safe and stable operation of transmission lines plays an important role in the normal operation of the entire power system. However, traditional monitoring methods for transmission line operation status face challenges such as limited accuracy, lack of real-time feedback, and high operational costs. In this paper, the Firefly algorithm is used to monitor the running status of transmission lines. Through synchronous testing with the traditional particle swarm optimization algorithm, it is found that the average accuracy of the Firefly algorithm in voltage and current measurement is improved to 93.13% and 93.66% respectively, which is better than the traditional algorithm. Firefly algorithm shows high precision in various power equipment monitoring, the average monitoring accuracy is 95.62% and 93.06%, respectively, which proves that it has stronger performance in transmission line monitoring and can achieve more stringent monitoring requirements. Through the comparison experiment of the algorithm, it proved that the Firefly algorithm had a strong performance in the transmission line operation status monitoring, and could more accurately identify the transmission line fault, which provided a new idea and new method for the safe operation status monitoring of transmission lines.
Keywords: Transmission line, firefly algorithm, state detection, particle swarm algorithm, power system
