Analysis of SF6 contact based on QPSO-SVR

• DOI: 10.1016/j.egyr.2023.03.020
• Publication Date: 2023-03-15T17:57:45Z
• Authors: Jianwei Bai; Peijun Cong; Hui Zhang; Shixun Sun; Bowen Zhang; Yong Wang; Liang Wang
• Keywords: Robustness; Contact resistance

In the condition evaluation of high-voltage SF6 circuit breaker, contact resistance and mass loss have a significant impact on arc contact. To that end, this paper proposes method based quantum particle swarm optimization support vector regression (QPSO-SVR), implementation which can effectively predict increment breaker contacts under different current conditions, best (SVR) algorithm training parameters are obtained through experimental data. validate proposed method's accuracy, it is compared to other prediction methods, results show QPSO-SVR has good predictive ability for data discharge parameters. The relative error 3.023%, 4.61%, indicating accuracy robustness. It serve as reference maintenance breakers, useful. great significance breaker.