Research on Leakage Monitoring of Metal Oxide Surge Arrester Based on Hybrid Particle Swarm Algorithm

Abstract

The leakage monitoring of the metal oxide arrester(MOA) is completed in the process of extracting the resistive component of the leakage current of the arrester, that is, the leakage monitoring of the arrester is realized by analyzing the operation of the resistive component of current. It is shown in relevant studies and literature that in the process of using metal oxide arrester, resistive component of current changes due to the aging of metal oxides. Therefore, the problem appears to be the leakage of the arrester. In view of this, aiming at the problem that previous intelligent algorithms are difficult to monitor the leakage of MOA accurately, this paper proposes an online monitoring algorithm based on hybrid particle swarm optimization by combining the classical particle swarm optimization algorithm and the nonlinear classification method. That is, after constructing the objective function of MOA, the characteristic parameters C and α which can effectively reflect the operation of MOA are solved. And then the current resistive component of the leakage current function equation is extracted to monitor the leakage of the metal oxide arrester. The research results show that in this study, the characteristic parameters C and α solved by the hybrid particle swarm algorithm are 502.19 and 24.9786 respectively, and the corresponding mean errors are 0.438% and 0.086% respectively. At the same time, the resistive current curve obtained by the hybrid particle swarm optimization algorithm is closer to the actual situation than that obtained by the particle swarm optimization algorithm. Thus, it can improve the accuracy of MOA Leakage Monitoring effectively.