Studies on the use of genetic algorithms to improve radial basis neural networks to monitor capacitive voltage transformers online

Article type: Research Article

Authors: Zang, Xiaokui^* | Cao, Zhiqiang | Xiao, Mengshi | Yang, Xiaoou

Affiliations: State Grid Xinjiang Marketing Service Center, Urumqi, Xinjiang, China

Correspondence: [*] Corresponding author: Xiaokui Zang, State Grid Xinjiang Marketing Service Center, Urumqi, Xinjiang 830000, China. Tel.: +86 18699122368; E-mail: [email protected].

Abstract: The metering accuracy and stability of the power grid transformer devices are of great concern to all parties of the power grid, power plants and large users, as well as the relevant government departments, with the further deepening of the electric power system reform and the gradual establishment of the electric power spot trading market. The outcomes demonstrate the viability and efficacy of the employed diagnosis method. The completed research can serve as a reference point for China’s development of a capacitive voltage transformer line monitoring system.

Keywords: Trading market, capacitive, voltage transformer, online monitoring, genetic algorithm, radial basis network

DOI: 10.3233/JCM-226756

Journal: Journal of Computational Methods in Sciences and Engineering, vol. 23, no. 4, pp. 1971-1983, 2023